title:
Galaxies and Halos on Graph Neural Networks: Deep Generative Modeling
  Scalar and Vector Quantities for Intrinsic Alignment

abstract:
  In order to prepare for the upcoming wide-field cosmological surveys, large
simulations of the Universe with realistic galaxy populations are required. In
particular, the tendency of galaxies to naturally align towards overdensities,
an effect called intrinsic alignments (IA), can be a major source of
systematics in the weak lensing analysis. As the details of galaxy formation
and evolution relevant to IA cannot be simulated in practice on such volumes,
we propose as an alternative a Deep Generative Model. This model is trained on
the IllustrisTNG-100 simulation and is capable of sampling the orientations of
a population of galaxies so as to recover the correct alignments. In our
approach, we model the cosmic web as a set of graphs, where the graphs are
constructed for each halo, and galaxy orientations as a signal on those graphs.
The generative model is implemented on a Generative Adversarial Network
architecture and uses specifically designed Graph-Convolutional Networks
sensitive to the relative 3D positions of the vertices. Given (sub)halo masses
and tidal fields, the model is able to learn and predict scalar features such
as galaxy and dark matter subhalo shapes; and more importantly, vector features
such as the 3D orientation of the major axis of the ellipsoid and the complex
2D ellipticities. For correlations of 3D orientations the model is in good
quantitative agreement with the measured values from the simulation, except for
at very small and transition scales. For correlations of 2D ellipticities, the
model is in good quantitative agreement with the measured values from the
simulation on all scales. Additionally, the model is able to capture the
dependence of IA on mass, morphological type and central/satellite type.


title: Impact of dark matter self-scattering on its relic abundance

abstract:
  Elastic self-scatterings do not change the number of dark matter particles
and as such have been neglected in the calculation of its relic abundance. In
this work we highlight the scenarios where the presence of self-scatterings has
a significant impact on the effectiveness of annihilation processes through the
modification of dark matter momentum distribution. We study a few example
freeze-out scenarios involving resonant and sub-threshold annihilations, as
well as a model with an additional source of dark matter particles from the
decays of a heavier mediator state. Interestingly, when the calculation is
performed at the level of dark matter momentum distribution function, we find
that the injection of additional energetic dark matter particles onto the
thermal population can lead to a $\textit{decrease}$ of its final relic
abundance.


title:
Cohesive zone modelling of hydrogen assisted fatigue crack growth: the
  role of trapping

abstract:
  We investigate the influence of microstructural traps in hydrogen-assisted
fatigue crack growth. To this end, a new formulation combining multi-trap
stress-assisted diffusion, mechanism-based strain gradient plasticity and a
hydrogen- and fatigue-dependent cohesive zone model is presented and
numerically implemented. The results show that the ratio of loading frequency
to effective diffusivity governs fatigue crack growth behaviour. Increasing the
density of \emph{beneficial} traps, not involved in the fracture process,
results in lower fatigue crack growth rates. The combinations of loading
frequency and carbide trap densities that minimise embrittlement susceptibility
are identified, providing the foundation for a rational design of
hydrogen-resistant alloys.


title:
Decentralized resolution of finite-state, non-convex, and aggregative
  optimal control problems

abstract:
  A general class of large-scale, nonconvex, and non-smooth optimization
problems is introduced. It has the form of a multi-agent problem, where the
agents interact through an aggregative term. A convex relaxation of the problem
is provided together with an estimate of the relaxation gap. A numerical
method, called stochastic Frank-Wolfe algorithm, is presented. The method
allows to find approximate solutions of the original problem in a decomposed
fashion. The convergence of the method is guaranteed from a theoretical point
of view. An aggregative deterministic optimal control problem is formulated,
with discrete state-space and discrete time. It is shown that the stochastic
Frank-Wolfe algorithm can be applied to the optimal control problem; in
particular, it amounts to solve at each iteration a series of small-scale
optimal control problems, corresponding to each agent. These sub-problems are
solved by dynamic programming. Numerical results are presented, for a toy model
of the charging management of a battery fleet.


title:
Boundary-bulk patterning in three-dimensional confluent cellular
  collectives

abstract:
  Organoids are in vitro cellular collectives from which brain-like, or
gut-like, or kidney-like structures emerge. To make quantitative predictions
regarding the morphology and rheology of a cellular collective in its initial
stages of development, we construct and study a three-dimensional vertex model.
In such a model, the cells are represented as deformable polyhedrons with cells
sharing faces such that there are no gaps between them, otherwise known as
confluent. In a bulk model with periodic boundary conditions, we find a
rigidity transition as a function of the target cell shape index $s_0$ with a
critical value $s_0^*=5.39\pm0.01$. For a confluent cellular collective with a
finite boundary, and in the presence of lateral extensile and in-plane, radial
extensile deformations, we find significant boundary-bulk patterning that is
one-cell layer thick. More specifically, for lateral extensile deformations,
the cells in the bulk are much less aligned with the direction of the lateral
deformation than the cells at the boundary. For in-plane, radial deformations,
the cells in the bulk exhibit much less reorientation perpendicular to the
radial direction than the cells at the boundary. In other words, for both
deformations, the bulk cells are insulated from the deformations, at least over
time scales much slower than the timescale for cellular rearrangements. Our
results provide an underlying mechanism for some observed cell shape patterning
in organoids. Finally, we discuss the use of a cellular-based approach to
designing organoids with new types of morphologies to study the intricate
relationship between structure and function at the multi-cellular scale.


title:
Dialogue Strategy Adaptation to New Action Sets Using Multi-dimensional
  Modelling

abstract:
  A major bottleneck for building statistical spoken dialogue systems for new
domains and applications is the need for large amounts of training data. To
address this problem, we adopt the multi-dimensional approach to dialogue
management and evaluate its potential for transfer learning. Specifically, we
exploit pre-trained task-independent policies to speed up training for an
extended task-specific action set, in which the single summary action for
requesting a slot is replaced by multiple slot-specific request actions. Policy
optimisation and evaluation experiments using an agenda-based user simulator
show that with limited training data, much better performance levels can be
achieved when using the proposed multi-dimensional adaptation method. We
confirm this improvement in a crowd-sourced human user evaluation of our spoken
dialogue system, comparing partially trained policies. The multi-dimensional
system (with adaptation on limited training data in the target scenario)
outperforms the one-dimensional baseline (without adaptation on the same amount
of training data) by 7% perceived success rate.


title: Direct Measurement of Higher-Order Nonlinear Polarization Squeezing

abstract:
  We report on nonlinear squeezing effects of polarization states of light by
harnessing the intrinsic correlations from a polarization-entangled light
source and click-counting measurements. To quantify these quantum effects, we
derive theoretical bounds for second- and higher-order moments of nonlinear
quantum Stokes operators, described in two polarization modes. The experimental
validation of our concept is rendered possible by developing an efficient
source, which uses a spectrally decorrelated type-II phase-matched waveguide
inside a Sagnac interferometer. Correlated click statistics and high-order
moments are directly obtained from an eight-time-bin
quasi-photon-number-resolving detection system. The generated macroscopic Bell
states that are readily available with our source show the distinct nature of
nonlinear polarization squeezing in up to eighth-order correlations, also
matching our theoretical predictions. Furthermore, our data certifies
nonclassical correlations with high statistical significance, without the need
to correct for experimental imperfections and limitations.


title: Spectral gap and stability for groups and non-local games

abstract:
  The word stable is used to describe a situation when mathematical objects
that almost satisfy an equation are close to objects satisfying it exactly. We
study operator-algebraic forms of stability for unitary representations of
groups and quantum synchronous strategies for non-local games. We observe in
particular that simple spectral gap estimates can lead to strong quantitative
forms of stability. For example, we prove that the direct product of two
(flexibly) Hilbert-Schmidt stable groups is again (flexibly) Hilbert-Schmidt
stable, provided that one of them has Kazhdan's property (T). We also provide a
simple form and simple analysis of a non-local game with few questions, with
the property that synchronous strategies with large value are close to perfect
strategies involving large Pauli matrices. This simplifies one of the steps
(the question reduction) in the recent announced resolution of Connes'
embedding problem by Ji, Natarajan, Vidick, Wright and Yuen.


title: Star vector fields on three-manifolds are multi-singular hyperbolic

abstract:
  The coexistence of singularities and regular orbits in chain transitive sets
has been a major obstacle in understanding the hyperbolic/partial hyperbolic
nature of robust dynamics. Notably, the vector fields with all periodic orbits
robustly hyperbolic (star flows), are hyperbolic in absence of singularities.
Morales, Pacifico and Pujals proposed a partial hyperbolicity called "singular
hyperbolicity" that characterizes an open and dense subset of three dimensional
star flows. In higher dimensions, Bonatti and da Luz characterize an open and
dense set of star vector fields by multi-singular hyperbolicity. In this
article, we prove that a vector field exhibiting all periodic orbits robustly
of the same index is multi-singular hyperbolic, generalizing the previous
results. As a corollary, we obtained that all three-dimensional star flows are
multi-singular hyperbolic. Moreover, if all singularities in the same class
exhibit the same index, the star flow is singular hyperbolic. Additionally,
star flows with robust chain recurrence classes in any dimension are
multisingular hyperbolic.


title:
Quantum Hall states for Rydberg atoms with laser-assisted dipole-dipole
  interactions

abstract:
  Rydberg atoms with dipole-dipole interactions provide intriguing platforms to
explore exotic quantum many-body physics. Here we propose a novel scheme with
laser-assisted dipole-dipole interactions to realize synthetic magnetic field
for Rydberg atoms in a two-dimensional array configuration, which gives rise to
the exotic bosonic topological states. In the presence of an external effective
Zeeman splitting gradient, the dipole-dipole interaction between neighboring
Rydberg atoms along the gradient direction is suppressed, but can be assisted
when Raman lights are applied to compensate the energy difference. With this
scheme we generate a controllable uniform magnetic field for the complex
spin-exchange coupling model, which can be mapped to hard core bosons coupling
to an external synthetic magnetic field. The highly tunable flat Chern bands of
the hard core bosons are then obtained and moreover, the bosonic fractional
quantum Hall states can be achieved with experimental feasibility. This work
opens an avenue for the realization of the highly-sought-after bosonic
topological orders using Rydberg atoms.

categories: cond-mat.mes-hall cond-mat.quant-gas cond-mat.str-el quant-ph


title:
Solution of the two-center Dirac equation with 20 digits precision using
  the finite-element technique

abstract:
  We present a precise fully relativistic numerical solution of the two-center
Coulomb problem. The special case of unit nuclear charges is relevant for the
accurate description of the ${\rm H}_2^+$ molecular ion and its isotopologues,
systems that are an active experimental topic. The computation utilizes the
2-spinor minmax approach and the finite-element method. The computed total
energies have estimated fractional uncertainties of a few times $10^{-20}$ for
unit charges and a bond length of 2 atomic units. The fractional uncertainty of
the purely relativistic contribution is $1\times10^{-17}$. The result is
relevant for future precision experiments, whereas at present the uncertainties
arising from the quantum electrodynamic treatment of the rovibrational
transition frequencies. are dominant.

categories: quant-ph physics.atom-ph physics.chem-ph physics.comp-ph


title: Superoscillations Made Super Simple

abstract:
  In ordinary circumstances the highest frequency present in a wave is the
highest frequency in its Fourier decomposition. It is however possible for
there to be a spatial or temporal region of the wave which locally oscillates
at a still greater frequency, in a phenomenon known as superoscillation.
Superoscillations find application in wide range of disciplines, but at present
their generation is based upon constructive approaches which are difficult to
implement. Here we address this, exploiting the fact that superoscillations are
a product of destructive interference to produce a prescription for generating
superoscillations from the superposition of arbitrary waveforms. As a first
test of the technique, we use it to combine four THz laser fields generated by
periodically poled Lithium Niobate. From this, we are able to predict and
observe for the first time THz optical superoscillations in the temporal
domain. The ability to generate superoscillations in this manner has potential
application in a wide range of fields. It may for example contribute to the
experimental realization of the complex pulses required by quantum control, and
the generation of attosecond pulses without resorting to nonlinear processes.


title:
Semiclassical WKB Problem for the non-self-adjoint Dirac operator with
  an analytic rapidly oscillating potential

abstract:
  In this paper we examine the semiclassical behavior of the scattering data of
a non-self-adjoint Dirac operator with a rapidly oscillating potential that is
complex analytic in some neighborhood of the real line. Some of our results are
rigorous and quite general. On the other hand, complete and concrete
understanding requires the investigation of the WKB geometry of specific
examples. For such detailed computations we use a particular example that has
been investigated numerically more than 20 years ago by Bronski and Miller and
rely heavily on their numerical computations. Mostly employing the exact WKB
method, we provide the complete rigorous uniform semiclassical analysis of the
Bohr-Sommerfeld condition for the location of the eigenvalues across unions of
analytic arcs as well as the associated norming constants. For the reflection
coefficient as well as the eigenvalues near 0 in the spectral plane, we employ
instead an older theory that has been developed in great detail by Olver. Our
analysis is motivated by the need to understand the semiclassical behaviour of
the focusing cubic NLS equation with initial data $A\exp\{iS/\epsilon\}$, in
view of the well-known fact discovered by Zakharov and Shabat that the spectral
analysis of the Dirac operator enables the solution of the NLS equation via
inverse scattering theory.


title:
Weakly Supervised Attended Object Detection Using Gaze Data as
  Annotations

abstract:
  We consider the problem of detecting and recognizing the objects observed by
visitors (i.e., attended objects) in cultural sites from egocentric vision. A
standard approach to the problem involves detecting all objects and selecting
the one which best overlaps with the gaze of the visitor, measured through a
gaze tracker. Since labeling large amounts of data to train a standard object
detector is expensive in terms of costs and time, we propose a weakly
supervised version of the task which leans only on gaze data and a frame-level
label indicating the class of the attended object. To study the problem, we
present a new dataset composed of egocentric videos and gaze coordinates of
subjects visiting a museum. We hence compare three different baselines for
weakly supervised attended object detection on the collected data. Results show
that the considered approaches achieve satisfactory performance in a weakly
supervised manner, which allows for significant time savings with respect to a
fully supervised detector based on Faster R-CNN. To encourage research on the
topic, we publicly release the code and the dataset at the following url:
https://iplab.dmi.unict.it/WS_OBJ_DET/


title: Numerical evaluation of dual norms via the MM algorithm

abstract:
  We deal with the problem of numerically computing the dual norm, which is
important to study sparsity-inducing regularizations (Jenatton et al. 2011,Bach
et al. 2012). The dual norms find application in optimization and statistical
learning, for example, in the design of working-set strategies, for
characterizing dual gradient methods, for dual decompositions and in the
definition of augmented Lagrangian functions. Nevertheless, the dual norm of
some well-known sparsity-inducing regolarization methods are not analytically
available. Examples are the overlap group $\ell_2$-norm of (Jenatton et al.
2011) and the elastic net norm of Zhou and Hastie (2005). Therefore we resort
to the Majorization-Minimization principle of Lange (2016) to provide an
efficient algorithm that leverages a reparametrization of the dual constrained
optimization problem as unconstrained optimization with barrier. Extensive
simulation experiments have been performed in order to verify the correctness
of operation, and evaluate the performance of the proposed method. Our results
demonstrate the effectiveness of the algorithm in retrieving the dual norm even
for large dimensions.


title:
RadioSES: mmWave-Based Audioradio Speech Enhancement and Separation
  System

abstract:
  Speech enhancement and separation have been a long-standing problem,
especially with the recent advances using a single microphone. Although
microphones perform well in constrained settings, their performance for speech
separation decreases in noisy conditions. In this work, we propose RadioSES, an
audioradio speech enhancement and separation system that overcomes inherent
problems in audio-only systems. By fusing a complementary radio modality,
RadioSES can estimate the number of speakers, solve source association problem,
separate and enhance noisy mixture speeches, and improve both intelligibility
and perceptual quality. We perform millimeter-wave sensing to detect and
localize speakers, and introduce an audioradio deep learning framework to fuse
the separate radio features with the mixed audio features. Extensive
experiments using commercial off-the-shelf devices show that RadioSES
outperforms a variety of state-of-the-art baselines, with consistent
performance gains in different environmental settings. Compared with the
audiovisual methods, RadioSES provides similar improvements (e.g., ~3 dB gains
in SiSDR), along with the benefits of lower computational complexity and being
less privacy concerning.


title: A Halmos-von Neumann theorem for actions of general groups

abstract:
  We give a new categorical approach to the Halmos-von Neumann theorem for
actions of general topological groups. As a first step, we establish that the
categories of topological and measure-preserving irreducible systems with
discrete spectrum are equivalent. This allows to prove the Halmos-von Neumann
theorem in the framework of topological dynamics. We then use the Pontryagin
and Tannaka-Krein duality theories to obtain classification results for
topological and then measure-preserving systems with discrete spectrum. As a
byproduct, we obtain a complete isomorphism invariant for compactifications of
a fixed topological group.


title: Form Factors in Higgs Couplings from Physics Beyond the Standard Model

abstract:
  We consider the momentum dependent effects in Higgs couplings generated by
physics beyond the standard model. We take a model dependent approach, in which
we can compute fully the non-local effects from physics not directly reachable
by the LHC energy. We consider several scenarios, including composite Higgs
models, additional scalars, and the continuum contributions of a
quasi-conformal sector, as examples. For each specific model, we obtain the
full form factor, with which one can eventually fully simulate the effects in
kinematics distributions. The momentum dependent effects appear as a
consequence of off-shellness in the process. We show how the sensitivity of
different channels to the various models depends on the flow of off-shellness
in the Higgs couplings.


title:
The SUNBIRD survey: the K-band luminosity functions of young massive
  clusters in intensely star-forming galaxies

abstract:
  Strongly star-forming galaxies are prolific in producing the young and most
massive star clusters (YMCs) still forming today. This work investigates the
star cluster luminosity functions (CLFs, dN/dL ~ L^{-alpha}) of 26 starburst
and luminous infrared galaxies (LIRGs) taken from the SUNBIRD survey. The
targets were imaged using near-infrared (NIR) K-band adaptive optics systems.
Single power-law fits of the derived CLFs result in a slope ranging between
1.53 and 2.41, with the median and average of 1.87 +/- 0.23 and 1.93 +/- 0.23,
respectively. Possible biases such as blending effects and the choice of
binning should only flatten the slope by no more than ~0.15, especially for
cases where the luminosity distance of the host galaxy is below 100 Mpc.
Results from this follow-up study strengthen the conclusion from our previous
work: the CLF slopes are shallower for strongly star-forming galaxies in
comparison to those with less intense star formation activity. There is also a
(mild) correlation between the slope and both the host galaxy's star formation
rate (SFR) and SFR density Sigma_SFR, i.e. the CLF flattens with an increasing
SFR and Sigma_SFR. Finally, we also find that CLFs on sub-galactic scales
associated with the nuclear regions of cluster-rich targets (N ~ 300) have
typically shallower slopes than the ones of the outer field by ~0.5. Our
analyses suggest that the extreme environments of strongly star-forming
galaxies are likely to influence the cluster formation mechanisms and
ultimately their physical properties.


title:
Detection of Degraded Acacia tree species using deep neural networks on
  uav drone imagery

abstract:
  Deep-learning-based image classification and object detection has been
applied successfully to tree monitoring. However, studies of tree crowns and
fallen trees, especially on flood inundated areas, remain largely unexplored.
Detection of degraded tree trunks on natural environments such as water,
mudflats, and natural vegetated areas is challenging due to the mixed colour
image backgrounds. In this paper, Unmanned Aerial Vehicles (UAVs), or drones,
with embedded RGB cameras were used to capture the fallen Acacia Xanthophloea
trees from six designated plots around Lake Nakuru, Kenya. Motivated by the
need to detect fallen trees around the lake, two well-established deep neural
networks, i.e. Faster Region-based Convolution Neural Network (Faster R-CNN)
and Retina-Net were used for fallen tree detection. A total of 7,590
annotations of three classes on 256 x 256 image patches were used for this
study. Experimental results show the relevance of deep learning in this
context, with Retina-Net model achieving 38.9% precision and 57.9% recall.


title:
Introducing a Symmetry-Breaking Coupler into a Dielectric Metasurface
  Enables Robust High-Q Quasibound States in the Continuum and Efficient
  Nonlinear Frequency Conversion

abstract:
  Dielectric metasurfaces supporting quasi-bound states in the continuum
(quasi-BICs) exhibit very high quality factor resonances and electric field
confinement. However, accessing the high-Q end of the quasi-BIC regime usually
requires marginally distorting the metasurface design from a BIC condition,
pushing the needed nanoscale fabrication precision to the limit. This work
introduces a novel concept for generating high-Q quasi-BICs, which strongly
relaxes this requirement by incorporating a relatively large perturbative
element close to high-symmetry points of an undistorted BIC metasurface, acting
as a coupler to the radiation continuum. We validate this approach by adding a
$\sim$100 nm diameter cylinder between two reflection-symmetry points separated
by a 300 nm gap in an elliptical disk metasurface unit cell, using gallium
phosphide as the dielectric. We find that high-Q resonances emerge when the
cylindrical coupler is placed at any position between such symmetry points. We
further explore this metasurface's second harmonic generation capability in the
optical range. Displacing the coupler as much as a full diameter from a BIC
condition produces record-breaking normalized conversion efficiencies >10$^{2}$
W$^{-1}$. The strategy of enclosing a disruptive element between multiple
high-symmetry points in a BIC metasurface could be applied to construct robust
high-Q quasi-BICs in many geometrical designs.


title:
Residual Swin Transformer Channel Attention Network for Image
  Demosaicing

abstract:
  Image demosaicing is problem of interpolating full- resolution color images
from raw sensor (color filter array) data. During last decade, deep neural
networks have been widely used in image restoration, and in particular, in
demosaicing, attaining significant performance improvement. In recent years,
vision transformers have been designed and successfully used in various
computer vision applications. One of the recent methods of image restoration
based on a Swin Transformer (ST), SwinIR, demonstrates state-of-the-art
performance with a smaller number of parameters than neural network-based
methods. Inspired by the success of SwinIR, we propose in this paper a novel
Swin Transformer-based network for image demosaicing, called RSTCANet. To
extract image features, RSTCANet stacks several residual Swin Transformer
Channel Attention blocks (RSTCAB), introducing the channel attention for each
two successive ST blocks. Extensive experiments demonstrate that RSTCANet out-
performs state-of-the-art image demosaicing methods, and has a smaller number
of parameters.


title:
Cosmological Parameter Estimation using $H(z)$ Measurements for Non-flat
  $\Lambda$ Cold Dark Matter Universe

abstract:
  Accurate estimations of cosmological density parameters
($\Omega_{0m}h_{0}^{2}$, $\Omega_{0k}h_{0}^{2}$, $\Omega_{0\Lambda}h_{0}^{2}$)
and Hubble constant ($h_{0}$) provide detailed understanding of our universe.
We propose a new procedure to estimate these parameters for non-flat
$\Lambda$CDM universe in the Friedmann-Robertson-Walker (FRW) background. We
utilize the Hubble parameter ($H(z)$) measurements in our analysis. These
$H(z)$ are measured using two techniques (DA and BAO). We generalize the
two-point diagnostic method first proposed by Sahni et al.(2008) to the case of
non-flat spatial curvature of the universe. Using three independent $H(z)$
measurements at a time, we solve for three fundamental cosmological density
parameters ($\Omega_{0m}h_{0}^{2}$, $\Omega_{0k}h_{0}^{2}$,
$\Omega_{0\Lambda}h_{0}^{2}$) and repeat the procedure for all possible
combinations of three measurements of Hubble parameters. We divide the $H(z)$
data into three groups comprising all, $31$ DA only and $24$ BAO only
measurements. We perform our analysis separately on each group. In our method,
we use weighted-mean and median statistics to estimate the values of density
parameters. Using these estimated values of density parameters, we also find
the values of $h_{0}$ for each of these three sets corresponding to each of two
statistics. We conclude that median statistic is more reliable in our analysis,
since the sample specific uncertainties are non-Gaussian in nature. Finally, we
achieve the reliable results $\Omega_{0m}h_{0}^{2}=0.1485^{+0.0041}_{-0.0051}$,
$\Omega_{0k}h_{0}^{2}=-0.0137^{+0.0129}_{-0.0125}$,
$\Omega_{0\Lambda}h_{0}^{2}=0.3126^{+0.0131}_{-0.009}$ and
$h_{0}=0.6689^{+0.0141}_{-0.0121}$ using median statistic in our analysis
corresponding to the set of total $53$ $H(z)$. Our estimated median values of
cosmological parameters agree with Planck Collaboration VI.(2020) results
excellently.


title:
Vacuum stability and scalar masses in the superweak extension of the
  standard model

abstract:
  We study the allowed parameter space of the scalar sector in the superweak
extension of the standard model (SM). The allowed region is defined by the
conditions of (i) stability of the vacuum and (ii) perturbativity up to the
Planck scale, (iii) the pole mass of the Higgs boson falls into its
experimentally measured range. We employ renormalization group equations and
quantum corrections at two-loop accuracy. We study the dependence on the Yukawa
couplings of the sterile neutrinos at selected values. We also check the
exclusion limit set by the precise measurement of the mass of the W boson. Our
method for constraining the parameter space using two-loop predictions can also
be applied to simpler models such as the singlet scalar extension of the SM in
a straightforward way.


title: Diffusions on Graph as Time Changed Multi-parameter Processes

abstract:
  Inspired by allocation strategies in multi-armed bandit model, we propose a
pathwise construction of diffusion processes on graphs. For any infinitesimal
generator on a connected graph without loops, there exists a unique time change
associated with a multi-parameter process such that the time change of this
multi-parameter process is the desired diffusion. The time change has an
interpretation of time allocation of the process on edges of the graph, and it
can be derived explicitly from a family of equations.


title: Synthesizing Analytical SQL Queries from Computation Demonstration

abstract:
  Analytical SQL is widely used in modern database applications and data
analysis. However, its partitioning and grouping operators are challenging for
novice users. Unfortunately, programming by example, shown effective on
standard SQL, are less attractive because examples for analytical queries are
more laborious to solve by hand.
  To make demonstrations easier to create, we designed a new end-user
specification, programming by computation demonstration, that allows the user
to demonstrate the task using a (possibly incomplete) cell-level computation
trace. This specification is exploited in a new abstraction-based synthesis
algorithm to prove that a partially formed query cannot be completed to satisfy
the specification, allowing us to prune the search space.
  We implemented our approach in a tool named Sickle and tested it on 80
real-world analytical SQL tasks. Results show that even from small
demonstrations, Sickle can solve 76 tasks, in 12.8 seconds on average, while
the prior approaches can solve only 60 tasks and are on average 22.5x slower.
Our user study with 13 participants reveals that our specification increases
user efficiency and confidence on challenging tasks.


title:
Extracting small time-lapse traveltime changes in a reservoir using
  primaries and internal multiples after Marchenko-based target zone isolation

abstract:
  Geophysical monitoring of subsurface reservoirs relies on detecting small
changes in the seismic response between a baseline and monitor study. However,
internal multiples, related to the over- and underburden, can obstruct the view
of the target response, hence complicating the time-lapse analysis. In order to
retrieve a response that is free from over- and underburden effects, the
data-driven Marchenko method is used. This method effectively isolates the
target response, which can then be used to extract more precise time-lapse
changes. Additionally, the method also reveals target-related multiples that
probe the reservoir more than once, which further define the changes in the
reservoir. To verify the effectiveness of the method, a numerical example is
constructed. This test shows that when using the isolated target response, the
observed time differences resemble the expected time differences in the
reservoir. Moreover, the results obtained with target-related multiples also
benefit from the Marchenko-based isolation of the reservoir. It is, therefore,
concluded that this method has potential to observe dynamic changes in the
subsurface with increased accuracy.


title:
cu_FastTucker: A Faster and Stabler Stochastic Optimization for Parallel
  Sparse Tucker Decomposition on Multi-GPUs

abstract:
  High-Order, High-Dimension, and Sparse Tensor (HOHDST) data originates from
real industrial applications, i.e., social networks, recommender systems,
bio-information, and traffic information. Sparse Tensor Decomposition (STD) can
project the HOHDST data into low-rank space. In this work, a novel method for
STD of Kruskal approximating the core tensor and stochastic strategy for
approximating the whole gradient is proposed which comprises of the following
two parts: (1) the matrization unfolding order of the Kruskal product for the
core tensor follows the multiplication order of the factor matrix and then the
proposed theorem can reduce the exponential computational overhead into linear
one; (2) stochastic strategy adopts one-step random sampling set, the volume of
which is much smaller than original one, to approximate the whole gradient.
Meanwhile, this method can guarantee the convergence and save the memory
overhead. Due to the compactness of the same order matrix multiplication and
parallel access from stochastic strategy, the speed of cuFastTucker can be
further reinforced by GPU. Furthermore, %because large-scale HOHDST data cannot
be accommodated in a single GPU, a data division and communication strategy of
cuFastTucker is proposed for data accommodation on Multi-GPU. cuFastTucker can
achieve the fastest speed and keep the same accuracy and much lower memory
overhead than the SOTA algorithms, e.g., P$-$Tucker, Vest, and SGD$\_$Tucker.
The code and partial datasets are publically available on
"https://github.com/ZixuanLi-China/FastTucker".


title: Nonresponse Bias Analysis in Longitudinal Educational Assessment Studies

abstract:
  Longitudinal studies are subject to nonresponse when individuals fail to
provide data for entire waves or particular questions of the survey. We compare
approaches to nonresponse bias analysis (NRBA) in longitudinal studies and
illustrate them on the Early Childhood Longitudinal Study, Kindergarten Class
of 2010-11 (ECLS-K:2011). Wave nonresponse with attrition yields a monotone
missingness pattern, and we discuss weighting and multiple imputation (MI)
approaches to NRBA for monotone patterns when the missingness mechanism is
assumed missing at random (MAR). Weighting adjustments are effective when the
constructed weights are correlated to the survey variable of interest. MI
allows for incomplete variables to be included in the imputation model,
yielding potentially less biased and more efficient estimates when the
variables are predictive of the survey outcome. Multilevel models with maximum
likelihood estimation and marginal models estimated using generalized
estimating equations can also handle incomplete longitudinal data. We add
offsets in the MI results to provide sensitivity analyses to assess missing not
at random deviations from MAR. We conduct NRBA for descriptive summaries and
analytic model estimates and find that in the ECLS-K:2011 application NRBA
yields minor changes to the substantive conclusions. The strength of evidence
about our NRBA depends on the strength of the relationship between the
characteristics in the nonresponse adjustment and the key survey variables, so
the key to a successful NRBA is to include strong predictors.


title: Symplectic Radon Transform and the Metaplectic Representation

abstract:
  We study the symplectic Radon transform from the point of view of the
metaplectic representation of the symplectic group and its action on the
Lagrangian Grassmannian. We give rigorous proofs in the general setting of
multi-dimensional quantum systems. We interpret the inverse Radon transform as
a "demarginalization process" for the Wigner distribution. This work completes,
by giving complete proofs, a previous Note.

categories: quant-ph math-ph math.FA math.MP math.RT


title:
A Study on Web Application Vulnerabilities to find an optimal Security
  Architecture

abstract:
  Over the past three decades, computers have managed to make their way into a
majority of households. Due to this enormous transition, the surge in the
internets popularity was inevitable. Just like everything else, whatever has a
pro also has a con, both are faces of the same coin. Nowadays, web security has
become one of the biggest challenges to the corporate world. All web
applications are prone to security vulnerabilities, its the developers job to
follow the latest norms in order to effectively reduce the threat posed by
unauthorized users or programs. In this paper, we have tried to analyze the
major problems faced in the real world and have tried to come up with effective
solutions to combat all the aforementioned problems.


title:
Water UV-shielding in the terrestrial planet-forming zone: Implications
  from water emission

abstract:
  Mid-infrared spectroscopy is one of the few ways to observe the composition
of the terrestial planet forming zone, the inner few au, of proto-planetary
disks. The species currently detected in the disk atmosphere, for example CO,
CO2, H2O and C2H2, are theoretically enough to constrain the C/O ratio in the
disk surface. However, thermo-chemical models have difficulties in reproducing
the full array of detected species in the mid-infrared simultaneously. In an
effort to get closer to the observed spectra, we have included water
UV-shielding as well as more efficient chemical heating into thermo-chemical
code Dust And Lines. We find that both are required to match the observed
emission spectrum. Efficient chemical heating, in addition to traditional
heating from UV photons, is necessary to elevate the temperature of the water
emitting layer to match the observed excitation temperature of water. We find
that water UV-shielding stops UV photons from reaching deep into the disk,
cooling down the lower layers with higher column. These two effects create a
hot emitting layer of water with a column of 1-10$\times 10^{18}$ cm$^{-2}$.
This is only 1-10% of the water column above the dust $\tau=1$ surface at
mid-infrared wavelengths in the models and represents <1% of the total water
column.


title: Improving the efficiency of learning-based error mitigation

abstract:
  Error mitigation will play an important role in practical applications of
near-term noisy quantum computers. Current error mitigation methods typically
concentrate on correction quality at the expense of frugality (as measured by
the number of additional calls to quantum hardware). To fill the need for
highly accurate, yet inexpensive techniques, we introduce an error mitigation
scheme that builds on Clifford data regression (CDR). The scheme improves the
frugality by carefully choosing the training data and exploiting the symmetries
of the problem. We test our approach by correcting long range correlators of
the ground state of XY Hamiltonian on IBM Toronto quantum computer. We find
that our method is an order of magnitude cheaper while maintaining the same
accuracy as the original CDR approach. The efficiency gain enables us to obtain
a factor of $10$ improvement on the unmitigated results with the total budget
as small as $2\cdot10^5$ shots.


title:
Generative power of a protein language model trained on multiple
  sequence alignments

abstract:
  Computational models starting from large ensembles of evolutionarily related
protein sequences capture a representation of protein families and learn
constraints associated to protein structure and function. They thus open the
possibility for generating novel sequences belonging to protein families.
Protein language models trained on multiple sequence alignments, such as MSA
Transformer, are highly attractive candidates to this end. We propose and test
an iterative method that directly uses the masked language modeling objective
to generate sequences using MSA Transformer. We demonstrate that the resulting
sequences generally score better than those generated by Potts models, and even
than natural sequences, for homology, coevolution and structure-based measures.
Moreover, MSA Transformer better reproduces the higher-order statistics and the
distribution of sequences in sequence space of natural data than Potts models,
although Potts models better reproduce first- and second-order statistics. MSA
Transformer is thus a strong candidate for protein sequence generation and
protein design.


title:
Position-Dependent Excitations and UV/IR Mixing in the $\mathbb{Z}_{N}$
  Rank-2 Toric Code and its Low-Energy Effective Field Theory

abstract:
  We investigate how symmetry and topological order are coupled in the ${2+1}$d
$\mathbb{Z}_{N}$ rank-2 toric code for general $N$, which is an exactly
solvable point in the Higgs phase of a symmetric rank-2 $U(1)$ gauge theory.
The symmetry enriched topological order present has a non-trivial realization
of square-lattice translation (and rotation/reflection) symmetry, where anyons
on different lattice sites have different types and belong to different
superselection sectors. We call such particles "position-dependent
excitations." As a result, in the rank-2 toric code anyons can hop by one
lattice site in some directions while only by $N$ lattice sites in others,
reminiscent of fracton topological order in ${3+1}$d. We find that while there
are $N^2$ flavors of $e$ charges and $2N$ flavors of $m$ fluxes, there are not
$N^{N^{2} + 2N}$ anyon types. Instead, there are $N^{6}$ anyon types, and we
can use Chern-Simons theory with six $U(1)$ gauge fields to describe all of
them. While the lattice translations permute anyon types, we find that such
permutations cannot be expressed as transformations on the six $U(1)$ gauge
fields. Thus the realization of translation symmetry in the $U^6(1)$
Chern-Simons theory is not known. Despite this, we find a way to calculate the
translation-dependent properties of the theory. In particular, we find that the
ground state degeneracy on an ${L_{x}\times L_{y}}$ torus is
${N^{3}\gcd(L_{x},N) \gcd(L_{y},N) \gcd(L_{x},L_{y},N)}$, where $\gcd$ stands
for "greatest common divisor." We argue that this is a manifestation of UV/IR
mixing which arises from the interplay between lattice symmetries and
topological order.


title:
A Formally Certified End-to-End Implementation of Shor's Factorization
  Algorithm

abstract:
  Quantum computing technology may soon deliver revolutionary improvements in
algorithmic performance, but these are only useful if computed answers are
correct. While hardware-level decoherence errors have garnered significant
attention, a less recognized obstacle to correctness is that of human
programming errors -- "bugs". Techniques familiar to most programmers from the
classical domain for avoiding, discovering, and diagnosing bugs do not easily
transfer, at scale, to the quantum domain because of its unique
characteristics. To address this problem, we have been working to adapt formal
methods to quantum programming. With such methods, a programmer writes a
mathematical specification alongside their program, and semi-automatically
proves the program correct with respect to it. The proof's validity is
automatically confirmed -- certified -- by a "proof assistant". Formal methods
have successfully yielded high-assurance classical software artifacts, and the
underlying technology has produced certified proofs of major mathematical
theorems. As a demonstration of the feasibility of applying formal methods to
quantum programming, we present the first formally certified end-to-end
implementation of Shor's prime factorization algorithm, developed as part of a
novel framework for applying the certified approach to general applications. By
leveraging our framework, one can significantly reduce the effects of human
errors and obtain a high-assurance implementation of large-scale quantum
applications in a principled way.


title:
Tuning nanosecond switching of spin-orbit torque driven magnetic tunnel
  junctions

abstract:
  Since the discovery of the spin orbit torque (SOT) induced by spin Hall
effect in heavy metals, much effort has been devoted to understanding the
mechanism of the charge-to-spin conversion as well as to developing new schemes
for high speed, low energy magnetic recording technologies. While fast
switching has been demonstrated in three terminal magnetic tunnel junctions
(3T-MTJs) through applying short voltage pulses in the heavy metal channel,
detailed understanding of the switching mechanism is lacking due to the
complexity of the multi-layered magnetic structure and the three-terminal
geometry. We show in this letter that current-induced effective fields play a
key role in the fast switching and by tuning the applied external field we can
finely tune the symmetry of the pulse switching between two switching
polarities, namely parallel to anti-parallel (P-AP) and anti-parallel to
parallel (AP-P). These results show that the manipulation of detailed magnetic
configuration is the key to fast switching and is a useful way for future
optimization of SOT memory and further applications.


title:
Look Back and Forth: Video Super-Resolution with Explicit Temporal
  Difference Modeling

abstract:
  Temporal modeling is crucial for video super-resolution. Most of the video
super-resolution methods adopt the optical flow or deformable convolution for
explicitly motion compensation. However, such temporal modeling techniques
increase the model complexity and might fail in case of occlusion or complex
motion, resulting in serious distortion and artifacts. In this paper, we
propose to explore the role of explicit temporal difference modeling in both LR
and HR space. Instead of directly feeding consecutive frames into a VSR model,
we propose to compute the temporal difference between frames and divide those
pixels into two subsets according to the level of difference. They are
separately processed with two branches of different receptive fields in order
to better extract complementary information. To further enhance the
super-resolution result, not only spatial residual features are extracted, but
the difference between consecutive frames in high-frequency domain is also
computed. It allows the model to exploit intermediate SR results in both future
and past to refine the current SR output. The difference at different time
steps could be cached such that information from further distance in time could
be propagated to the current frame for refinement. Experiments on several video
super-resolution benchmark datasets demonstrate the effectiveness of the
proposed method and its favorable performance against state-of-the-art methods.


title: Risk measures under model uncertainty: a Bayesian viewpoint

abstract:
  We introduce two kinds of risk measures with respect to some reference
probability measure, which both allow for a certain order structure and
domination property. Analyzing their relation to each other leads to the
question when a certain minimax inequality is actually an equality. We then
provide conditions under which the corresponding robust risk measures, being
defined as the supremum over all risk measures induced by a set of probability
measures, can be represented classically in terms of one single probability
measure. We focus in particular on the mixture probability measure obtained via
mixing over a set of probability measures using some prior, which represents
for instance the regulator's beliefs. The classical representation in terms of
the mixture probability measure can then be interpreted as a Bayesian approach
to robust risk measures.


title: Spherical varieties and non-ordinary families of cohomology classes

abstract:
  We give a construction of non-ordinary $p$-adic families of cohomology
classes. We give criteria for \'etale classes to be mapped into into Galois
cohomology and show how this can be used to interpolate the Lemma-Flach Euler
system in Coleman families.


title:
A 5.7 THz GaN/AlGaN quantum cascade detector based on polar step quantum
  wells

abstract:
  We report on a GaN/AlGaN quantum cascade detector operating in the terahertz
spectral range. The device was grown by metal organic chemical vapor deposition
on a c-sapphire substrate and relies on polar GaN/AlGaN step quantum wells. The
active region thickness is in micrometer range. The structural, electrical and
optical investigations attest of high structural quality of the synthetized
nitride material. The detector exhibits a peak photocurrent at 5.7 THz (23.6
meV) with a responsivity of 0.1 mA/W at 10 K under surface normal irradiation
through a 10 um period grating. The photocurrent persists up to 20 K.


title: DeiT III: Revenge of the ViT

abstract:
  A Vision Transformer (ViT) is a simple neural architecture amenable to serve
several computer vision tasks. It has limited built-in architectural priors, in
contrast to more recent architectures that incorporate priors either about the
input data or of specific tasks. Recent works show that ViTs benefit from
self-supervised pre-training, in particular BerT-like pre-training like BeiT.
In this paper, we revisit the supervised training of ViTs. Our procedure builds
upon and simplifies a recipe introduced for training ResNet-50. It includes a
new simple data-augmentation procedure with only 3 augmentations, closer to the
practice in self-supervised learning. Our evaluations on Image classification
(ImageNet-1k with and without pre-training on ImageNet-21k), transfer learning
and semantic segmentation show that our procedure outperforms by a large margin
previous fully supervised training recipes for ViT. It also reveals that the
performance of our ViT trained with supervision is comparable to that of more
recent architectures. Our results could serve as better baselines for recent
self-supervised approaches demonstrated on ViT.


title: Loop representation and r-Fock measures for $SU(N)$ gauge theories

abstract:
  In this article we continue the work on translating elements of the
perturbative quantum field theory defined on Minkowski spacetime into the
background independent framework of Loop Quantum Gravity. We present the
construction of r-Fock measures for $SU(N)$ gauge theories and provide a
relation between these new r-Fock measures and the difeomorphism invariant
measure used in loop quantum gravity.


title: Exploring Dual Encoder Architectures for Question Answering

abstract:
  Dual encoders have been used for question-answering (QA) and information
retrieval (IR) tasks with good results. There are two major types of dual
encoders, Siamese Dual Encoders (SDE), with parameters shared across two
encoders, and Asymmetric Dual Encoder (ADE), with two distinctly parameterized
encoders. In this work, we explore the dual encoder architectures for QA
retrieval tasks. By evaluating on MS MARCO and the MultiReQA benchmark, we show
that SDE performs significantly better than ADE. We further propose three
different improved versions of ADEs. Based on the evaluation of QA retrieval
tasks and direct analysis of the embeddings, we demonstrate that sharing
parameters in projection layers would enable ADEs to perform competitively with
SDEs.


title: Facing the Illusion and Reality of Safety in Social VR

abstract:
  The ethical design of social Virtual Reality (VR) is not a new topic, but
"safety" concerns of using social VR are escalated to a different level given
the heat of the Metaverse. For example, it was reported that nearly half of the
female-identifying VR participants have had at least one instance of virtual
sexual harassment. Feeling safe is a basic human right - in any place,
regardless in real or virtual spaces. In this paper, we are seeking to
understand the discrepancy between user concerns and designs in protecting user
safety in social VR applications. We study safety concerns on social VR
experience first by analyzing Twitter posts and then synthesize practices on
safety protection adopted by four mainstream social VR platforms. We argue that
future research and platforms should explore the design of social VR with
boundary-awareness.


title: MIMO Channel Estimation using Score-Based Generative Models

abstract:
  Channel estimation is a critical task in multiple-input multiple-output
digital communications that has effects on end-to-end system performance. In
this work, we introduce a novel approach for channel estimation using deep
score-based generative models. These models are trained to estimate the
gradient of the log-prior distribution, and can be used to iteratively refine
estimates, given observed measurements of a signal. We introduce a framework
for training score-based generative models for wireless channels, as well as
performing channel estimation using posterior sampling at test time. We derive
theoretical robustness guarantees of channel estimation with posterior sampling
in single-input single-output scenarios, and show that the observations
regarding estimation performance are verified experimentally in MIMO channels.
Our results in simulated clustered delay line channels show competitive
in-distribution performance without error floors in the high signal-to-noise
ratio regime, and robust out-of-distribution performance, outperforming
competing deep learning methods by up to 5 dB in end-to-end communication
performance, while the complexity analysis reveals how model architecture can
efficiently trade performance for estimation latency.


title:
Retrospective on the 2021 BASALT Competition on Learning from Human
  Feedback

abstract:
  We held the first-ever MineRL Benchmark for Agents that Solve Almost-Lifelike
Tasks (MineRL BASALT) Competition at the Thirty-fifth Conference on Neural
Information Processing Systems (NeurIPS 2021). The goal of the competition was
to promote research towards agents that use learning from human feedback (LfHF)
techniques to solve open-world tasks. Rather than mandating the use of LfHF
techniques, we described four tasks in natural language to be accomplished in
the video game Minecraft, and allowed participants to use any approach they
wanted to build agents that could accomplish the tasks. Teams developed a
diverse range of LfHF algorithms across a variety of possible human feedback
types. The three winning teams implemented significantly different approaches
while achieving similar performance. Interestingly, their approaches performed
well on different tasks, validating our choice of tasks to include in the
competition. While the outcomes validated the design of our competition, we did
not get as many participants and submissions as our sister competition, MineRL
Diamond. We speculate about the causes of this problem and suggest improvements
for future iterations of the competition.


title:
Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in
  Medicine

abstract:
  Dynamic treatment regimes (DTRs) are used in medicine to tailor sequential
treatment decisions to patients by considering patient heterogeneity. Common
methods for learning optimal DTRs, however, have shortcomings: they are
typically based on outcome prediction and not treatment effect estimation, or
they use linear models that are restrictive for patient data from modern
electronic health records. To address these shortcomings, we develop two novel
methods for learning optimal DTRs that effectively handle complex patient data.
We call our methods DTR-CT and DTR-CF. Our methods are based on a data-driven
estimation of heterogeneous treatment effects using causal tree methods,
specifically causal trees and causal forests, that learn non-linear
relationships, control for time-varying confounding, are doubly robust, and
explainable. To the best of our knowledge, our paper is the first that adapts
causal tree methods for learning optimal DTRs. We evaluate our proposed methods
using synthetic data and then apply them to real-world data from intensive care
units. Our methods outperform state-of-the-art baselines in terms of cumulative
regret and percentage of optimal decisions by a considerable margin. Our work
improves treatment recommendations from electronic health record and is thus of
direct relevance for personalized medicine.


title:
Online Aggregation based Approximate Query Processing: A Literature
  Survey

abstract:
  In the current world, OLAP (Online Analytical Processing) is used intensively
by modern organizations to perform ad hoc analysis of data, providing insight
for better decision making. Thus, the performance for OLAP is crucial; however,
it is costly to support OLAP for a large data-set. An approximate query process
(AQP) was proposed to efficiently compute approximate values as close as to the
exact answer. Existing AQP techniques can be categorized into two parts, online
aggregation, and offline synopsis generation, each having its limitations and
challenges. Online aggregation-based AQP progressively generates approximate
results with some error estimates (i.e., confidence interval) until the
processing of all data is done. In Offline synopsis generation-based AQP,
synopses are generated offline using a-priori knowledge such as query workload
or data statistics. Later, OLAP queries are answered using these synopses. This
paper focuses on surveying only the online aggregation-based AQP. For this
purpose, firstly, we discuss the research challenges in online
aggregation-based AQP and summarize existing approaches to address these
challenges. In addition, we also discuss the advantages and limitations of
existing online aggregation mechanisms. Lastly, we discuss some research
challenges and opportunities for further advancing online aggregation research.
Our goal is for people to understand the current progress in the online
aggregation-based AQP area and find new insights into it.


title: GIFS: Neural Implicit Function for General Shape Representation

abstract:
  Recent development of neural implicit function has shown tremendous success
on high-quality 3D shape reconstruction. However, most works divide the space
into inside and outside of the shape, which limits their representing power to
single-layer and watertight shapes. This limitation leads to tedious data
processing (converting non-watertight raw data to watertight) as well as the
incapability of representing general object shapes in the real world. In this
work, we propose a novel method to represent general shapes including
non-watertight shapes and shapes with multi-layer surfaces. We introduce
General Implicit Function for 3D Shape (GIFS), which models the relationships
between every two points instead of the relationships between points and
surfaces. Instead of dividing 3D space into predefined inside-outside regions,
GIFS encodes whether two points are separated by any surface. Experiments on
ShapeNet show that GIFS outperforms previous state-of-the-art methods in terms
of reconstruction quality, rendering efficiency, and visual fidelity. Project
page is available at https://jianglongye.com/gifs .


title:
Relaxation dynamics and long-time tails explain shear-induced diffusion
  of soft athermal particles near jamming

abstract:
  We numerically study shear-induced diffusion of soft athermal particles in
two dimensions. The Green-Kubo (GK) relation is applicable to diffusion
coefficient of the particles near jamming, where both mean squared particle
velocities and relaxation time included in the GK formula are well explained by
critical scaling. We show that auto-correlation functions of the transverse
velocities are stretched exponential if the system is below jamming or shear
rate is large enough. However, if the system is above jamming and the shear
rate is sufficiently small, the auto-correlations exhibit long-time tails such
that time integral in the GK formula diverges in two dimensions. We propose
empirical scaling relations for the critical exponents and demonstrate that the
long-time tails cause finite size effects on the shear-induced diffusion
coefficient.


title: Label Semantic Aware Pre-training for Few-shot Text Classification

abstract:
  In text classification tasks, useful information is encoded in the label
names. Label semantic aware systems have leveraged this information for
improved text classification performance during fine-tuning and prediction.
However, use of label-semantics during pre-training has not been extensively
explored. We therefore propose Label Semantic Aware Pre-training (LSAP) to
improve the generalization and data efficiency of text classification systems.
LSAP incorporates label semantics into pre-trained generative models (T5 in our
case) by performing secondary pre-training on labeled sentences from a variety
of domains. As domain-general pre-training requires large amounts of data, we
develop a filtering and labeling pipeline to automatically create
sentence-label pairs from unlabeled text. We perform experiments on intent
(ATIS, Snips, TOPv2) and topic classification (AG News, Yahoo! Answers). LSAP
obtains significant accuracy improvements over state-of-the-art models for
few-shot text classification while maintaining performance comparable to state
of the art in high-resource settings.


title:
On The Complexity of Matching Cut for Graphs of Bounded Radius and
  $H$-Free Graphs

abstract:
  For a connected graph $G=(V,E)$, a matching $M\subseteq E$ is a matching cut
of $G$ if $G-M$ is disconnected. It is known that for an integer $d$, the
corresponding decision problem Matching Cut is polynomial-time solvable for
graphs of diameter at most $d$ if $d\leq 2$ and NP-complete if $d\geq 3$. We
prove the same dichotomy for graphs of bounded radius. For a graph $H$, a graph
is $H$-free if it does not contain $H$ as an induced subgraph. As a consequence
of our result, we can solve Matching Cut in polynomial time for $P_6$-free
graphs, extending a recent result of Feghali for $P_5$-free graphs. We then
extend our result to hold even for $(sP_3+P_6)$-free graphs for every $s\geq 0$
and initiate a complexity classification of Matching Cut for $H$-free graphs.


title: Non-perturbative Double Copy in Flatland

abstract:
  We derive a non-perturbative, Lagrangian-level formulation of the double copy
in two spacetime dimensions. Our results elucidate the field theoretic
underpinnings of the double copy in a broad class of scalar theories which can
include masses and higher-dimension operators. An immediate corollary is the
amplitudes-level double copy at all orders in perturbation theory. Applied to
certain integrable models, the double copy defines an isomorphism between Lax
connections, Wilson lines, and infinite towers of conserved currents. We also
implement the double copy at the level of non-perturbative classical solutions,
both analytically and numerically, and present a stringy generalization.


title:
Systematic Analysis of Experiment Precision Measures and Methods for
  Experiments Comparison

abstract:
  The notion of experiment precision quantifies the variance of user ratings in
a subjective experiment. Although there exist measures assessing subjective
experiment precision, there are no systematic analyses of these measures
available in the literature. To the best of our knowledge, there is also no
systematic framework in the Multimedia Quality Assessment (MQA) field for
comparing subjective experiments in terms of their precision. Therefore, the
main idea of this paper is to propose a framework for comparing subjective
experiments in the field of MQA based on appropriate experiment precision
measures. We put forward three experiment precision measures and three related
experiment precision comparison methods. We systematically analyse the
performance of the measures and methods proposed. We do so both through a
stimulation study (varying user rating variance and bias) and by using data
from three real-world Quality of Experience (QoE) subjective experiments. In
the simulation study we focus on crowdsourcing QoE experiments, since they are
known to generate ratings with higher variance and bias, when compared to
traditional subjective experiment methodologies. We conclude that our proposed
measures and related comparison methods properly capture experiment precision
(both when tested on simulated and real-world data). One of the measures also
proves capable of dealing with even significantly biased responses. We believe
our experiment precision assessment framework will help compare different
subjective experiment methodologies. For example, it may help decide which
methodology results in more precise user ratings. This may potentially inform
future standardisation activities.


title: On Merino--Welsh conjecture for split matroids

abstract:
  In 1999 Merino and Welsh conjectured that evaluations of the Tutte polynomial
of a graph satisfy an inequality. In this short article we show that the
conjecture generalized to matroids holds for the large class of all split
matroids by exploiting the structure of their lattice of cyclic flats. This
class of matroids strictly contains all paving and copaving matroids.


title:
Fundamental solution of the Vladimirov-Taibleson operator on
  noncommutative Vilenkin groups

abstract:
  The fundamental solution and the heat semigroup of the Vladimirov-Taibleson
operator on constant-order noncommutative Vilenkin groups are obtained,
together with some estimates on the associated heat kernel. We also show the
existence of a fundamental solution for the "Vladimirov Laplacian" on the
$p$-adic Heisenberg group and the $p$-adic Engel group, and discuss possible
extensions of our results to more general homogeneous operators on graded
$p$-adic Lie groups.


title:
Reinforcement Learning Policy Recommendation for Interbank Network
  Stability

abstract:
  In this paper we analyze the effect of a policy recommendation on the
performances of an artificial interbank market. Financial institutions
stipulate lending agreements following a public recommendation and their
individual information. The former, modeled by a reinforcement learning optimal
policy trying to maximize the long term fitness of the system, gathers
information on the economic environment and directs economic actors to create
credit relationships based on the optimal choice between a low interest rate or
high liquidity supply. The latter, based on the agents' balance sheet, allows
to determine the liquidity supply and interest rate that the banks optimally
offer on the market. Based on the combination between the public and the
private signal, financial institutions create or cut their credit connections
over time via a preferential attachment evolving procedure able to generate a
dynamic network. Our results show that the emergence of a core-periphery
interbank network, combined with a certain level of homogeneity on the size of
lenders and borrowers, are essential features to ensure the resilience of the
system. Moreover, the reinforcement learning optimal policy recommendation
plays a crucial role in mitigating systemic risk with respect to alternative
policy instruments.


title:
Scalable and Robust Self-Learning for Skill Routing in Large-Scale
  Conversational AI Systems

abstract:
  Skill routing is an important component in large-scale conversational
systems. In contrast to traditional rule-based skill routing, state-of-the-art
systems use a model-based approach to enable natural conversations. To provide
supervision signal required to train such models, ideas such as human
annotation, replication of a rule-based system, relabeling based on user
paraphrases, and bandit-based learning were suggested. However, these
approaches: (a) do not scale in terms of the number of skills and skill
on-boarding, (b) require a very costly expert annotation/rule-design, (c)
introduce risks in the user experience with each model update. In this paper,
we present a scalable self-learning approach to explore routing alternatives
without causing abrupt policy changes that break the user experience, learn
from the user interaction, and incrementally improve the routing via frequent
model refreshes. To enable such robust frequent model updates, we suggest a
simple and effective approach that ensures controlled policy updates for
individual domains, followed by an off-policy evaluation for making deployment
decisions without any need for lengthy A/B experimentation. We conduct various
offline and online A/B experiments on a commercial large-scale conversational
system to demonstrate the effectiveness of the proposed method in real-world
production settings.


title:
Constraining nucleosynthesis in neutrino-driven winds: observations,
  simulations and nuclear physics

abstract:
  A promising astrophysical site to produce the lighter heavy elements of the
first $r$-process peak ($Z = 38-47$) is the moderately neutron rich ($0.4 < Y_e
< 0.5$) neutrino-driven ejecta of explosive environments, such as core-collapse
supernovae and neutron star mergers, where the weak $r$-process operates. This
nucleosynthesis exhibits uncertainties from the absence of experimental data
from $(\alpha,xn)$ reactions on neutron-rich nuclei, which are currently based
on statistical model estimates. In this work, we report on a new study of the
nuclear reaction impact using a Monte Carlo approach and improved $(\alpha,xn)$
rates based on the Atomki-V2 $\alpha$ Optical Model Potential ($\alpha$OMP). We
compare our results with observations from an up-to-date list of metal-poor
stars with [Fe/H] $<$ -1.5 to find conditions of the neutrino-driven wind where
the lighter heavy elements can be synthesized. We identified a list of
$(\alpha,xn)$ reaction rates that affect key elemental ratios in different
astrophysical conditions. Our study aims on motivating more nuclear physics
experiments on $(\alpha, xn)$ reactions using current and the new generation of
radioactive beam facilities and also more observational studies of metal-poor
stars.


title: Accelerated Policy Learning with Parallel Differentiable Simulation

abstract:
  Deep reinforcement learning can generate complex control policies, but
requires large amounts of training data to work effectively. Recent work has
attempted to address this issue by leveraging differentiable simulators.
However, inherent problems such as local minima and exploding/vanishing
numerical gradients prevent these methods from being generally applied to
control tasks with complex contact-rich dynamics, such as humanoid locomotion
in classical RL benchmarks. In this work we present a high-performance
differentiable simulator and a new policy learning algorithm (SHAC) that can
effectively leverage simulation gradients, even in the presence of
non-smoothness. Our learning algorithm alleviates problems with local minima
through a smooth critic function, avoids vanishing/exploding gradients through
a truncated learning window, and allows many physical environments to be run in
parallel. We evaluate our method on classical RL control tasks, and show
substantial improvements in sample efficiency and wall-clock time over
state-of-the-art RL and differentiable simulation-based algorithms. In
addition, we demonstrate the scalability of our method by applying it to the
challenging high-dimensional problem of muscle-actuated locomotion with a large
action space, achieving a greater than 17x reduction in training time over the
best-performing established RL algorithm.


title: Type III seesaw with R-parity violation in light of $m_W$ (CDF)

abstract:
  Motivated by the recently reported measurement of the $W$ boson mass $M_W =
80.4335 \pm 0.0094$ GeV by the CDF collaboration, we propose a type III
extension of the minimal supersymmetric standard model (MSSM) which also
includes an R-parity violating term. Without taking potential SUSY radiative
corrections into account, we show that the CDF measurement of $M_W$ and the LEP
measurement of the $\rho$ parameter can be simultaneously accommodated at the
$2 \sigma$ level. A long-lived gravitino in a few GeV mass range is a unique
viable dark matter candidate in this framework.


title:
The images of multilinear and semihomogeneous polynomials on the algebra
  of octonions

abstract:
  The generalized L'vov-Kaplansky conjecture states that for any
finite-dimensional simple algebra $A$ the image of a multilinear polynomial on
$A$ is a vector space. In this paper we prove it for the algebra of octonions
$\mathbb{O}$ over a field satisfying certain specified conditions (in
particular, we prove it for quadratically closed field and for field
$\mathbb{R}$).
  In fact, we prove that the image set must be either $\{0\}$, $F$, the space
of pure octonions $V$, or $\mathbb{O}$. We discuss possible evaluations of
semihomogeneous polynomials on $\mathbb{O}$ and of arbitrary polynomials on the
corresponding Malcev algebra.


title: De-projecting the EFThedron

abstract:
  The space of Wilson coefficients of EFT that can be UV completed into
consistent theories was recently shown to be described analytically by a
positive geometry, termed the EFThedron. However, this geometry, as well as
complementary numerical methods of semi-definite programming, have so far
focused on the positivity of the partial wave expansion, which allows bounding
only ratios of couplings. In this paper we describe how the unitarity upper
bound of the partial waves can be incorporated. This new problem can be
formulated in terms of the well known $L$-moment problem, which we generalize
and solve from a geometrical perspective. We find the non-projective
generalization of the EFThedron has an infinite number of non-linear facets,
which in some cases have remarkably simple descriptions. We use these results
to derive bounds on single couplings, finding that the leading derivative
operators are bounded by unity, when normalized by the cut-off scale and loop
factors. For general operators of mass dimension $2k$ we find the upper bound
is heavily suppressed at large $k$, with an $1/k$ fall-off.


title: Masked Siamese Networks for Label-Efficient Learning

abstract:
  We propose Masked Siamese Networks (MSN), a self-supervised learning
framework for learning image representations. Our approach matches the
representation of an image view containing randomly masked patches to the
representation of the original unmasked image. This self-supervised
pre-training strategy is particularly scalable when applied to Vision
Transformers since only the unmasked patches are processed by the network. As a
result, MSNs improve the scalability of joint-embedding architectures, while
producing representations of a high semantic level that perform competitively
on low-shot image classification. For instance, on ImageNet-1K, with only 5,000
annotated images, our base MSN model achieves 72.4% top-1 accuracy, and with 1%
of ImageNet-1K labels, we achieve 75.7% top-1 accuracy, setting a new
state-of-the-art for self-supervised learning on this benchmark. Our code is
publicly available.


title:
CLUES: A Benchmark for Learning Classifiers using Natural Language
  Explanations

abstract:
  Supervised learning has traditionally focused on inductive learning by
observing labeled examples of a task. In contrast, humans have the ability to
learn new concepts from language. Here, we explore training zero-shot
classifiers for structured data purely from language. For this, we introduce
CLUES, a benchmark for Classifier Learning Using natural language ExplanationS,
consisting of a range of classification tasks over structured data along with
natural language supervision in the form of explanations. CLUES consists of 36
real-world and 144 synthetic classification tasks. It contains crowdsourced
explanations describing real-world tasks from multiple teachers and
programmatically generated explanations for the synthetic tasks. To model the
influence of explanations in classifying an example, we develop ExEnt, an
entailment-based model that learns classifiers using explanations. ExEnt
generalizes up to 18% better (relative) on novel tasks than a baseline that
does not use explanations. We delineate key challenges for automated learning
from explanations, addressing which can lead to progress on CLUES in the
future. Code and datasets are available at: https://clues-benchmark.github.io.


title: Neighborhood Attention Transformer

abstract:
  We present Neighborhood Attention Transformer (NAT), an efficient, accurate
and scalable hierarchical transformer that works well on both image
classification and downstream vision tasks. It is built upon Neighborhood
Attention (NA), a simple and flexible attention mechanism that localizes the
receptive field for each query to its nearest neighboring pixels. NA is a
localization of self-attention, and approaches it as the receptive field size
increases. It is also equivalent in FLOPs and memory usage to Swin
Transformer's shifted window attention given the same receptive field size,
while being less constrained. Furthermore, NA includes local inductive biases,
which eliminate the need for extra operations such as pixel shifts.
Experimental results on NAT are competitive; NAT-Tiny reaches 83.2% top-1
accuracy on ImageNet with only 4.3 GFLOPs and 28M parameters, 51.4% mAP on
MS-COCO and 48.4% mIoU on ADE20k. We will open-source our checkpoints, training
script, configurations, and our CUDA kernel at:
https://github.com/SHI-Labs/Neighborhood-Attention-Transformer .


title: On the $W$-mass and New Higgs Bosons

abstract:
  We discuss the prediction of the $W$ mass in a simple extension of the
Standard Model ($\SSM$) with a real scalar triplet. A shift in the $W$ mass as
reported by the CDF II collaboration can naturally be accommodated by the model
without modifying the Standard Model value for the $Z$ mass. We discuss the
main implications and the properties of the new Higgs bosons. Namely, the
partial decay widths of the new charged Higgs are predicted. Furthermore, the
neutral Higgs has suppressed couplings to fermions and decays predominantly
into a pair of $W$ gauge bosons.


title:
Performance of optimal linear-response processes in driven Brownian
  motion far from equilibrium

abstract:
  Considering the paradigmatic example of driven Brownian motion, we perform
extensive numerical analysis on the performance of optimal linear-response
processes far from equilibrium. We restrict ourselves to the overdamped regime
where exact optimal processes are known analytically and several experimental
realizations have been implemented. This allows us to make a fair comparison
with the optimal processes obtained in linear response and address their
relevance to experiments. The investigations we present use parameters values
taken from feasible experimental setups. Our results also help to delimit the
range of validity of the perturbative descriptions of the irreversible work.
Nevertheless, our main result is the fact that optimal linear-response
processes can perform surprisingly well far from their expected regime of
application.

categories: cond-mat.stat-mech cond-mat.mes-hall cond-mat.soft physics.bio-ph


title:
GelSight Fin Ray: Incorporating Tactile Sensing into a Soft Compliant
  Robotic Gripper

abstract:
  To adapt to constantly changing environments and be safe for human
interaction, robots should have compliant and soft characteristics as well as
the ability to sense the world around them. Even so, the incorporation of
tactile sensing into a soft compliant robot, like the Fin Ray finger, is
difficult due to its deformable structure. Not only does the frame need to be
modified to allow room for a vision sensor, which enables intricate tactile
sensing, the robot must also retain its original mechanically compliant
properties. However, adding high-resolution tactile sensors to soft fingers is
difficult since many sensorized fingers, such as GelSight-based ones, are rigid
and function under the assumption that changes in the sensing region are only
from tactile contact and not from finger compliance. A sensorized soft robotic
finger needs to be able to separate its overall proprioceptive changes from its
tactile information. To this end, this paper introduces the novel design of a
GelSight Fin Ray, which embodies both the ability to passively adapt to any
object it grasps and the ability to perform high-resolution tactile
reconstruction, object orientation estimation, and marker tracking for shear
and torsional forces. Having these capabilities allow soft and compliant robots
to perform more manipulation tasks that require sensing. One such task the
finger is able to perform successfully is a kitchen task: wine glass
reorientation and placement, which is difficult to do with external vision
sensors but is easy with tactile sensing. The development of this sensing
technology could also potentially be applied to other soft compliant grippers,
increasing their viability in many different fields.


title:
Floquet-heating-induced non-equilibrium Bose condensation in an open
  optical lattice

abstract:
  Periodically driven quantum systems suffer from heating via resonant
excitation. While such Floquet heating guides a generic isolated system towards
the infinite temperature state, a driven open system, coupled to a thermal
bath, will approach a non-equilibrium steady state, which is determined by the
interplay of driving and dissipation. Here, we show that this interplay can
give rise to the counterintuitive effect that Floquet heating can induce Bose
condensation. We consider a one-dimensional Bose gas in an optical lattice of
finite extent, which is coupled weakly to a three-dimensional thermal bath
given by a second atomic species. The bath temperature $T$ lies well above the
crossover temperature, below which the majority of the system's particles form
a (finite-size) Bose condensate in the ground state. However, when a strong
local potential modulation is switched on, which resonantly excites the system,
a non-equilibrium Bose condensate is formed in a state that decouples from the
drive. Our predictions, which are based on a microscopic model that is solved
using kinetic equations of motion derived from Floquet-Born-Markov theory, can
be probed under realistic experimental conditions.


title: Quantifying protocols for safe school activities

abstract:
  By the peak of COVID-19 restrictions on April 8, 2020, up to 1.5 billion
students across 188 countries were by the suspension of physical attendance in
schools. Schools were among the first services to reopen as vaccination
campaigns advanced. With the emergence of new variants and infection waves, the
question now is to find safe protocols for the continuation of school
activities. We need to understand how reliable these protocols are under
different levels of vaccination coverage, as many countries have a meager
fraction of their population vaccinated, including Uganda where the coverage is
about 8\%. We investigate the impact of face-to-face classes under different
protocols and quantify the surplus number of infected individuals in a city.
Using the infection transmission when schools were closed as a baseline, we
assess the impact of physical school attendance in classrooms with poor air
circulation. We find that (i) resuming school activities with people only
wearing low-quality masks leads to a near fivefold city-wide increase in the
number of cases even if all staff is vaccinated, (ii) resuming activities with
students wearing good-quality masks and staff wearing N95s leads to about a
threefold increase, (iii) combining high-quality masks and active monitoring,
activities may be carried out safely even with low vaccination coverage. These
results highlight the effectiveness of good mask-wearing. Compared to ICU
costs, high-quality masks are inexpensive and can help curb the spreading.
Classes can be carried out safely, provided the correct set of measures are
implemented.


title: An Integrated Design Pipeline for Tactile Sensing Robotic Manipulators

abstract:
  Traditional robotic manipulator design methods require extensive,
time-consuming, and manual trial and error to produce a viable design. During
this process, engineers often spend their time redesigning or reshaping
components as they discover better topologies for the robotic manipulator.
Tactile sensors, while useful, often complicate the design due to their bulky
form factor. We propose an integrated design pipeline to streamline the design
and manufacturing of robotic manipulators with knitted, glove-like tactile
sensors. The proposed pipeline allows a designer to assemble a collection of
modular, open-source components by applying predefined graph grammar rules. The
end result is an intuitive design paradigm that allows the creation of new
virtual designs of manipulators in a matter of minutes. Our framework allows
the designer to fine-tune the manipulator's shape through cage-based geometry
deformation. Finally, the designer can select surfaces for adding tactile
sensing. Once the manipulator design is finished, the program will
automatically generate 3D printing and knitting files for manufacturing. We
demonstrate the utility of this pipeline by creating four custom manipulators
tested on real-world tasks: screwing in a wing screw, sorting water bottles,
picking up an egg, and cutting paper with scissors.


title: FREDA: Flexible Relation Extraction Data Annotation

abstract:
  To effectively train accurate Relation Extraction models, sufficient and
properly labeled data is required. Adequately labeled data is difficult to
obtain and annotating such data is a tricky undertaking. Previous works have
shown that either accuracy has to be sacrificed or the task is extremely
time-consuming, if done accurately. We are proposing an approach in order to
produce high-quality datasets for the task of Relation Extraction quickly.
Neural models, trained to do Relation Extraction on the created datasets,
achieve very good results and generalize well to other datasets. In our study,
we were able to annotate 10,022 sentences for 19 relations in a reasonable
amount of time, and trained a commonly used baseline model for each relation.


title: Deformable Sprites for Unsupervised Video Decomposition

abstract:
  We describe a method to extract persistent elements of a dynamic scene from
an input video. We represent each scene element as a \emph{Deformable Sprite}
consisting of three components: 1) a 2D texture image for the entire video, 2)
per-frame masks for the element, and 3) non-rigid deformations that map the
texture image into each video frame. The resulting decomposition allows for
applications such as consistent video editing. Deformable Sprites are a type of
video auto-encoder model that is optimized on individual videos, and does not
require training on a large dataset, nor does it rely on pre-trained models.
Moreover, our method does not require object masks or other user input, and
discovers moving objects of a wider variety than previous work. We evaluate our
approach on standard video datasets and show qualitative results on a diverse
array of Internet videos. Code and video results can be found at
https://deformable-sprites.github.io


title: Enhanced Gravitational Waves from Inflaton Oscillons

abstract:
  In broad classes of inflationary models the period of accelerated expansion
is followed by fragmentation of the inflaton scalar field into localized,
long-lived and massive oscillon excitations. We demonstrate that
matter-dominance of oscillons, followed by their rapid decay, significantly
enhances the primordial gravitational wave (GW) spectrum. These
oscillon-induced GWs are distinct and could be orders of magnitude lower in
frequency than the previously considered GWs associated with oscillon
formation. We show that detectable oscillon-induced GW signatures establish
direct tests independent from cosmic microwave background radiation (CMB) for
monodromy, logarithmic and pure natural (plateau) potential classes of
inflationary models, among others. We demonstrate that oscillon-induced GWs in
a model based on pure natural inflation could be directly observable with the
Einstein Telescope, Cosmic Explorer and DECIGO. These signatures offer a new
route for probing the underlying inflationary physics.


title: What's in your hands? 3D Reconstruction of Generic Objects in Hands

abstract:
  Our work aims to reconstruct hand-held objects given a single RGB image. In
contrast to prior works that typically assume known 3D templates and reduce the
problem to 3D pose estimation, our work reconstructs generic hand-held object
without knowing their 3D templates. Our key insight is that hand articulation
is highly predictive of the object shape, and we propose an approach that
conditionally reconstructs the object based on the articulation and the visual
input. Given an image depicting a hand-held object, we first use off-the-shelf
systems to estimate the underlying hand pose and then infer the object shape in
a normalized hand-centric coordinate frame. We parameterized the object by
signed distance which are inferred by an implicit network which leverages the
information from both visual feature and articulation-aware coordinates to
process a query point. We perform experiments across three datasets and show
that our method consistently outperforms baselines and is able to reconstruct a
diverse set of objects. We analyze the benefits and robustness of explicit
articulation conditioning and also show that this allows the hand pose
estimation to further improve in test-time optimization.


title: MiniViT: Compressing Vision Transformers with Weight Multiplexing

abstract:
  Vision Transformer (ViT) models have recently drawn much attention in
computer vision due to their high model capability. However, ViT models suffer
from huge number of parameters, restricting their applicability on devices with
limited memory. To alleviate this problem, we propose MiniViT, a new
compression framework, which achieves parameter reduction in vision
transformers while retaining the same performance. The central idea of MiniViT
is to multiplex the weights of consecutive transformer blocks. More
specifically, we make the weights shared across layers, while imposing a
transformation on the weights to increase diversity. Weight distillation over
self-attention is also applied to transfer knowledge from large-scale ViT
models to weight-multiplexed compact models. Comprehensive experiments
demonstrate the efficacy of MiniViT, showing that it can reduce the size of the
pre-trained Swin-B transformer by 48\%, while achieving an increase of 1.0\% in
Top-1 accuracy on ImageNet. Moreover, using a single-layer of parameters,
MiniViT is able to compress DeiT-B by 9.7 times from 86M to 9M parameters,
without seriously compromising the performance. Finally, we verify the
transferability of MiniViT by reporting its performance on downstream
benchmarks. Code and models are available at here.


title:
Tight Bounds for Quantum State Certification with Incoherent
  Measurements

abstract:
  We consider the problem of quantum state certification, where we are given
the description of a mixed state $\sigma \in \mathbb{C}^{d \times d}$, $n$
copies of a mixed state $\rho \in \mathbb{C}^{d \times d}$, and $\varepsilon >
0$, and we are asked to determine whether $\rho = \sigma$ or whether $\| \rho -
\sigma \|_1 > \varepsilon$. When $\sigma$ is the maximally mixed state
$\frac{1}{d} I_d$, this is known as mixedness testing. We focus on algorithms
which use incoherent measurements, i.e. which only measure one copy of $\rho$
at a time. Unlike those that use entangled, multi-copy measurements, these can
be implemented without persistent quantum memory and thus represent a large
class of protocols that can be run on current or near-term devices.
  For mixedness testing, there is a folklore algorithm which uses incoherent
measurements and only needs $O(d^{3/2} / \varepsilon^2)$ copies. The algorithm
is non-adaptive, that is, its measurements are fixed ahead of time, and is
known to be optimal for non-adaptive algorithms. However, when the algorithm
can make arbitrary incoherent measurements, the best known lower bound is only
$\Omega (d^{4/3} / \varepsilon^2)$ [Bubeck-Chen-Li '20], and it has been an
outstanding open problem to close this polynomial gap. In this work, 1) we
settle the copy complexity of mixedness testing with incoherent measurements
and show that $\Omega (d^{3/2} / \varepsilon^2)$ copies are necessary, and 2)
we show the instance-optimal bounds for state certification to general $\sigma$
first derived by [Chen-Li-O'Donnell '21] for non-adaptive measurements also
hold for arbitrary incoherent measurements.
  Qualitatively, our results say that adaptivity does not help at all for these
problems. Our results are based on new techniques that allow us to reduce the
problem to understanding certain matrix martingales, which we believe may be of
independent interest.


title: Any-resolution Training for High-resolution Image Synthesis

abstract:
  Generative models operate at fixed resolution, even though natural images
come in a variety of sizes. As high-resolution details are downsampled away,
and low-resolution images are discarded altogether, precious supervision is
lost. We argue that every pixel matters and create datasets with variable-size
images, collected at their native resolutions. Taking advantage of this data is
challenging; high-resolution processing is costly, and current architectures
can only process fixed-resolution data. We introduce continuous-scale training,
a process that samples patches at random scales to train a new generator with
variable output resolutions. First, conditioning the generator on a target
scale allows us to generate higher resolutions images than previously possible,
without adding layers to the model. Second, by conditioning on continuous
coordinates, we can sample patches that still obey a consistent global layout,
which also allows for scalable training at higher resolutions. Controlled FFHQ
experiments show our method takes advantage of the multi-resolution training
data better than discrete multi-scale approaches, achieving better FID scores
and cleaner high-frequency details. We also train on other natural image
domains including churches, mountains, and birds, and demonstrate arbitrary
scale synthesis with both coherent global layouts and realistic local details,
going beyond 2K resolution in our experiments. Our project page is available
at: https://chail.github.io/anyres-gan/.


title: Joint Forecasting of Panoptic Segmentations with Difference Attention

abstract:
  Forecasting of a representation is important for safe and effective autonomy.
For this, panoptic segmentations have been studied as a compelling
representation in recent work. However, recent state-of-the-art on panoptic
segmentation forecasting suffers from two issues: first, individual object
instances are treated independently of each other; second, individual object
instance forecasts are merged in a heuristic manner. To address both issues, we
study a new panoptic segmentation forecasting model that jointly forecasts all
object instances in a scene using a transformer model based on 'difference
attention.' It further refines the predictions by taking depth estimates into
account. We evaluate the proposed model on the Cityscapes and AIODrive
datasets. We find difference attention to be particularly suitable for
forecasting because the difference of quantities like locations enables a model
to explicitly reason about velocities and acceleration. Because of this, we
attain state-of-the-art on panoptic segmentation forecasting metrics.


title:
A non-associative incidence near-ring with a generalized M\"obius
  function

abstract:
  There is a convolution product on 3-variable partial flag functions of a
locally finite poset that produces a generalized M\"obius function. Under the
product this generalized M\"obius function is a one sided inverse of the zeta
function and satisfies many generalizations of classical results. In particular
we prove analogues of Phillip Hall's Theorem on the M\"obius function as an
alternating sum of chain counts, Weisner's theorem, and Rota's Crosscut
Theorem. A key ingredient to these results is that this function is an
overlapping product of classical M\"obius functions. Using this generalized
M\"obius function we define analogues of the characteristic polynomial and
M\"obius polynomials for ranked lattices. We compute these polynomials for
certain families of matroids and prove that this generalized M\"obius
polynomial has -1 as root if the matroid is modular. Using results from Ardila
and Sanchez we prove that this generalized characteristic polynomial is a
matroid valuation.


title: A Level Set Theory for Neural Implicit Evolution under Explicit Flows

abstract:
  Coordinate-based neural networks parameterizing implicit surfaces have
emerged as efficient representations of geometry. They effectively act as
parametric level sets with the zero-level set defining the surface of interest.
We present a framework that allows applying deformation operations defined for
triangle meshes onto such implicit surfaces. Several of these operations can be
viewed as energy-minimization problems that induce an instantaneous flow field
on the explicit surface. Our method uses the flow field to deform parametric
implicit surfaces by extending the classical theory of level sets. We also
derive a consolidated view for existing methods on differentiable surface
extraction and rendering, by formalizing connections to the level-set theory.
We show that these methods drift from the theory and that our approach exhibits
improvements for applications like surface smoothing, mean-curvature flow,
inverse rendering and user-defined editing on implicit geometry.