title:
Adaptive Spike-Like Representation of EEG Signals for Sleep Stages
  Scoring

abstract:
  Recently there has seen promising results on automatic stage scoring by
extracting spatio-temporal features from electroencephalogram (EEG). Such
methods entail laborious manual feature engineering and domain knowledge. In
this study, we propose an adaptive scheme to probabilistically encode, filter
and accumulate the input signals and weight the resultant features by the
half-Gaussian probabilities of signal intensities. The adaptive representations
are subsequently fed into a transformer model to automatically mine the
relevance between features and corresponding stages. Extensive experiments on
the largest public dataset against state-of-the-art methods validate the
effectiveness of our proposed method and reveal promising future directions.


title:
LIGO-Virgo detector characterization and data quality (DetChar): from
  the O3 performance to the O4 preparation

abstract:
  Detector characterization and data quality -- in short "DetChar" --
activities are key to optimize the performance of data-taking periods ("runs")
and to turn gravitational-wave (GW) candidates into confirmed events. The LIGO
and Virgo DetChar groups are active from the detector to the final analysis and
cover various latencies: online first, to tag the data that search pipelines
can analyze in real-time; then, the quick vetting (few tens of minutes at most)
of the open public alerts targeting the broad astronomer community for
follow-up observations; finally, offline work to define the final datasets and
the final lists of GW events to be published and released publicly.
  These proceedings summarize the LIGO-Virgo DetChar performance during the O3
run (April 2019 - March 2020) and describe the main improvements and upgrades
that are foreseen for the O4 run that should start during Summer 2022 and
include a fourth detector: KAGRA.


title: A local phase space stochastic quantization?

abstract:
  I examine whether Nelson's stochastic formulation of Schr\"{o}dinger equation
could be derived from a phase space process through a colored noise smoothing.
If this conjecture is true, it would yield a local stochastic hidden variable
theory. I discuss how this does not necessarily contradict Bell type theorems
as general local stochastic theories can violate local causality assumptions. I
also discuss the generalization to quantization of fields and speculate about
the gravitational origins of noise.


title: Precession of perihelia in the Fisher metric

abstract:
  We study the precession of perihelia in the Fisher metric. Fisher metric is
the solution of the Einstein's Equations with a massless scalar field as a
coupling. We find an expression for the precession of perihelia in this metric.
This expression contains general relativistic term for the precession of the
perihelia and also an additional term which depends on the scalar field. Also,
we obtain an upper bound on scalar charge $\sigma$ by using the observational
value of the precession of perihelia for the Mercury planet and the discrepancy
between this value and the general relativistic value.


title:
Faster algorithms for learning to link, align sequences, and price
  two-part tariffs

abstract:
  Data-driven algorithm configuration is a promising, learning-based approach
for beyond worst-case analysis of algorithms with tunable parameters. An
important open problem is the design of efficient data-driven algorithms for
algorithm families with more than one parameter. In this work we provide
algorithms for efficient (output-polynomial) multidimensional parameter tuning,
i.e. for families with a small constant number of parameters, for three very
different combinatorial problems -- linkage-based clustering, dynamic
programming for sequence alignment, and auction design for two-part tariff
schemes. We extend the single-parameter clustering algorithm of Balcan et al.
2020 arXiv:1907.00533 to multiple parameters and to the sequence alignment
problem by proposing an execution graph which compactly represents all the
states the algorithm could attain for all possible parameter values. A key
problem-specific challenge is to efficiently compute how the partition of the
parameter space (into regions with unique algorithmic states) changes with a
single algorithmic step. We give algorithms which improve on the runtime of
previously best known results for linkage-based clustering, sequence alignment
and two-part tariff pricing.


title:
Improving Urban Mobility: using artificial intelligence and new
  technologies to connect supply and demand

abstract:
  As the demand for mobility in our society seems to increase, the various
issues centered on urban mobility are among those that worry most city
inhabitants in this planet. For instance, how to go from A to B in an efficient
(but also less stressful) way? These questions and concerns have not changed
even during the covid-19 pandemic; on the contrary, as the current stand,
people who are avoiding public transportation are only contributing to an
increase in the vehicular traffic. The are of intelligent transportation
systems (ITS) aims at investigating how to employ information and communication
technologies to problems related to transportation. This may mean monitoring
and managing the infrastructure (e.g., traffic roads, traffic signals, etc.).
However, currently, ITS is also targeting the management of demand. In this
panorama, artificial intelligence plays an important role, especially with the
advances in machine learning that translates in the use of computational
vision, connected and autonomous vehicles, agent-based simulation, among
others. In the present work, a survey of several works developed by our group
are discussed in a holistic perspective, i.e., they cover not only the supply
side (as commonly found in ITS works), but also the demand side, and, in an
novel perspective, the integration of both.


title:
Explicit and Implicit Pattern Relation Analysis for Discovering
  Actionable Negative Sequences

abstract:
  Real-life events, behaviors and interactions produce sequential data. An
important but rarely explored problem is to analyze those nonoccurring (also
called negative) yet important sequences, forming negative sequence analysis
(NSA). A typical NSA area is to discover negative sequential patterns (NSPs)
consisting of important non-occurring and occurring elements and patterns. The
limited existing work on NSP mining relies on frequentist and downward closure
property-based pattern selection, producing large and highly redundant NSPs,
nonactionable for business decision-making. This work makes the first attempt
for actionable NSP discovery. It builds an NSP graph representation, quantify
both explicit occurrence and implicit non-occurrence-based element and pattern
relations, and then discover significant, diverse and informative NSPs in the
NSP graph to represent the entire NSP set for discovering actionable NSPs. A
DPP-based NSP representation and actionable NSP discovery method EINSP
introduces novel and significant contributions for NSA and sequence analysis:
(1) it represents NSPs by a determinantal point process (DPP) based graph; (2)
it quantifies actionable NSPs in terms of their statistical significance,
diversity, and strength of explicit/implicit element/pattern relations; and (3)
it models and measures both explicit and implicit element/pattern relations in
the DPP-based NSP graph to represent direct and indirect couplings between NSP
items, elements and patterns. We substantially analyze the effectiveness of
EINSP in terms of various theoretical and empirical aspects including
complexity, item/pattern coverage, pattern size and diversity, implicit pattern
relation strength, and data factors.


title:
A Pathology-Based Machine Learning Method to Assist in Epithelial
  Dysplasia Diagnosis

abstract:
  The Epithelial Dysplasia (ED) is a tissue alteration commonly present in
lesions preceding oral cancer, being its presence one of the most important
factors in the progression toward carcinoma. This study proposes a method to
design a low computational cost classification system to support the detection
of dysplastic epithelia, contributing to reduce the variability of pathologist
assessments. We employ a multilayer artificial neural network (MLP-ANN) and
defining the regions of the epithelium to be assessed based on the knowledge of
the pathologist. The performance of the proposed solution was statistically
evaluated. The implemented MLP-ANN presented an average accuracy of 87%, with a
variability much inferior to that obtained from three trained evaluators.
Moreover, the proposed solution led to results which are very close to those
obtained using a convolutional neural network (CNN) implemented by transfer
learning, with 100 times less computational complexity. In conclusion, our
results show that a simple neural network structure can lead to a performance
equivalent to that of much more complex structures, which are routinely used in
the literature.


title:
An optimized hybrid solution for IoT based lifestyle disease
  classification using stress data

abstract:
  Stress, anxiety, and nervousness are all high-risk health states in everyday
life. Previously, stress levels were determined by speaking with people and
gaining insight into what they had experienced recently or in the past.
Typically, stress is caused by an incidence that occurred a long time ago, but
sometimes it is triggered by unknown factors. This is a challenging and complex
task, but recent research advances have provided numerous opportunities to
automate it. The fundamental features of most of these techniques are electro
dermal activity (EDA) and heart rate values (HRV). We utilized an accelerometer
to measure body motions to solve this challenge. The proposed novel method
employs a test that measures a subject's electrocardiogram (ECG), galvanic skin
values (GSV), HRV values, and body movements in order to provide a low-cost and
time-saving solution for detecting stress lifestyle disease in modern times
using cyber physical systems. This study provides a new hybrid model for
lifestyle disease classification that decreases execution time while picking
the best collection of characteristics and increases classification accuracy.
The developed approach is capable of dealing with the class imbalance problem
by using WESAD (wearable stress and affect dataset) dataset. The new model uses
the Grid search (GS) method to select an optimized set of hyper parameters, and
it uses a combination of the Correlation coefficient based Recursive feature
elimination (CoC-RFE) method for optimal feature selection and gradient
boosting as an estimator to classify the dataset, which achieves high accuracy
and helps to provide smart, accurate, and high-quality healthcare systems. To
demonstrate the validity and utility of the proposed methodology, its
performance is compared to those of other well-established machine learning
models.


title: Learning to Compose Soft Prompts for Compositional Zero-Shot Learning

abstract:
  We introduce compositional soft prompting (CSP), a parameter-efficient
learning technique to improve the zero-shot compositionality of large-scale
pretrained vision-language models (VLMs) without the overhead of fine-tuning
the entire model. VLMs can represent arbitrary classes as natural language
prompts in their flexible text encoders but they underperform state-of-the-art
methods on compositional zero-shot benchmark tasks. To improve VLMs, we propose
a novel form of soft prompting. We treat the attributes and objects that are
composed to define classes as learnable tokens of vocabulary and tune them on
multiple prompt compositions. During inference, we recompose the learned
attribute-object vocabulary in new combinations and show that CSP outperforms
the original VLM on benchmark datasets by an average of 14.7 percentage points
of accuracy. CSP also achieves new state-of-the-art accuracies on two out of
three benchmark datasets, while only fine-tuning a small number of parameters.
Further, we show that CSP improves generalization to higher-order
attribute-attribute-object compositions and combinations of pretrained
attributes and fine-tuned objects.


title:
Preconditioning for a Phase-Field Model with Application to Morphology
  Evolution in Organic Semiconductors

abstract:
  The Cahn--Hilliard equations are a versatile model for describing the
evolution of complex morphologies. In this paper we present a computational
pipeline for the numerical solution of a ternary phase-field model for
describing the nanomorphology of donor--acceptor semiconductor blends used in
organic photovoltaic devices. The model consists of two coupled fourth-order
partial differential equations that are discretized using a finite element
approach. In order to solve the resulting large-scale linear systems
efficiently, we propose a preconditioning strategy that is based on efficient
approximations of the Schur-complement of a saddle point system. We show that
this approach performs robustly with respect to variations in the
discretization parameters. Finally, we outline that the computed morphologies
can be used for the computation of charge generation, recombination, and
transport in organic solar cells.


title:
Synthesizing longitudinal cortical thickness estimates with a flexible
  and hierarchical multivariate measurement-error model

abstract:
  MRI-based entorhinal cortical thickness (eCT) measurements predict cognitive
decline in Alzheimer's disease (AD) with low cost and minimal invasiveness. Two
prominent imaging paradigms, FreeSurfer (FS) and Advanced Normalization Tools
(ANTs), feature multiple pipelines for extracting region-specific eCT
measurements from raw MRI, but the sheer complexity of these pipelines makes it
difficult to choose between pipelines, compare results between pipelines, and
characterize uncertainty in pipeline estimates. Worse yet, the EC is
particularly difficult to image, leading to variations in thickness estimates
between pipelines that overwhelm physiologicl variations predictive of AD. We
examine the eCT outputs of seven different pipelines on MRIs from the
Alzheimer's Disease Neuroimaging Initiative. Because of both theoretical and
practical limitations, we have no gold standard by which to evaluate them.
Instead, we use a Bayesian hierarchical model to combine the estimates. The
resulting posterior distribution yields high-probability idealized eCT values
that account for inherent uncertainty through a flexible multivariate error
model that supports different constant offsets, standard deviations,
tailedness, and correlation structures between pipelines. Our hierarchical
model directly relates idealized eCTs to clinical outcomes in a way that
propagates eCT estimation uncertainty to clinical estimates while accounting
for longitudinal structure in the data. Surprisingly, even though it
incorporates greater uncertainty in the predictor and regularization provided
by the prior, the combined model reveals a stronger association between eCT and
cognitive capacity than do nonhierarchical models based on data from single
pipelines alone.


title: Satisfiability transition in asymmetric neural networks

abstract:
  Asymmetry in the synaptic interactions between neurons plays a crucial role
in determining the memory storage and retrieval properties of recurrent neural
networks. In this work, we analyze the problem of storing random memories in a
network of neurons connected by a synaptic matrix with a definite degree of
asymmetry. We study the corresponding satisfiability and clustering transitions
in the space of solutions of the constraint satisfaction problem associated
with finding synaptic matrices given the memories. We find, besides the usual
SAT/UNSAT transition at a critical number of memories to store in the network,
an additional transition for very asymmetric matrices, where the competing
constraints (definite asymmetry vs. memories storage) induce enough frustration
in the problem to make it impossible to solve. This finding is particularly
striking in the case of a single memory to store, where no quenched disorder is
present in the system.


title:
On the Interaction Between Electromagnetic, Gravitational, and Plasma
  Related Perturbations on LRS Class II Spacetimes

abstract:
  We investigate electromagnetic, gravitational, and plasma related
perturbations to first order on homogeneous and hypersurface orthogonal locally
rotationally symmetric (LRS) class II spacetimes. Due to the anisotropic nature
of the studied backgrounds, we are able to include a non-zero magnetic field to
zeroth order. As a result of this inclusion, we find interesting interactions
between the electromagnetic and gravitational variables already to first order
in the perturbations. The equations governing these perturbations are found by
using the Ricci identities, the Bianchi identities, Einstein's field equations,
Maxwell's equations, particle conservation, and a form of energy-momentum
conservation for the plasma components. Using a $1+1+2$ covariant split of
spacetime, the studied quantities and equations are decomposed with respect to
the preferred directions on the background spacetimes. After linearizing the
decomposed equations around a LRS background, performing a harmonic
decomposition, and imposing the cold magnetohydrodynamic (MHD) limit with a
finite electrical resistivity, the system is then reduced to a set of ordinary
differential equations in time and some constraints. On solving for some of the
harmonic coefficients in terms of the others, the system is found to decouple
into two closed and independent subsectors. Through numerical calculations, we
then observe some mechanisms for generating magnetic field perturbations,
showing some traits similar to previous works using FLRW backgrounds.
Furthermore, beat-like patterns are observed in the short wave length limit due
to interference between gravitational waves and plasmonic modes.


title: Partitions of topological spaces and a new club-like principle

abstract:
  We give a new proof of the following theorem due to W. Weiss and P. Komjath:
if $X$ is a regular topological space, with character $ < \mathfrak{b}$ and $X
\rightarrow (top \omega + 1)^{1}_{\omega}$, then, for all $\alpha < \omega_1$,
$X \rightarrow (top \alpha)^{1}_{\omega}$, fixing a gap in the original one.
For that we consider a new decomposition of topological spaces. We also define
a new combinatorial principle $\clubsuit_{F}$, and use it to prove that it is
consistent with $\neg CH$ that $\mathfrak{b}$ is the optimal bound for the
character of $X$. In \cite{WeissKomjath}, this was obtained using
$\diamondsuit$.


title:
Controlling the electrostatic Faraday instability using superposed
  electric fields

abstract:
  When the interface between a dielectric and a conducting liquid is excited by
an oscillatory electric field, electrostatic Faraday waves can be induced.
Here, we study the response of the interface to an AC electric field, which is
superposed by either a second AC field of different frequency, or by a DC
field. An algorithm based on light refraction at the fluidic interface is used
to obtain the spatio-temporal response of the Faraday waves, and the critical
voltage corresponding to onset of instability, the interfacial oscillation
frequency and the dominant wavelengths are determined. The influence of the
mixing ratio, which denotes the relative amplitudes of the different components
of the driving signal, is analyzed, and the experimental results are compared
with theoretical predictions. For AC/AC driving, gradual variations of the
mixing ratio can induce a jump of the pattern wavelength, which is a result of
the transition from harmonic to a subharmonic oscillation. For AC/DC driving,
the interface oscillates either harmonically or subharmonically, and the
response wavelength can be tuned continuously by adjusting the admixture of the
DC component. For both driving modes, the experiments show good agreement with
theory.


title: Idempotent linear relations

abstract:
  A linear relation $E$ acting on a Hilbert space is idempotent if $E^2=E.$ A
triplet of subspaces is needed to characterize a given idempotent:
$(\mathrm{ran} \, E, \mathrm{ran}(I-E), \mathrm{dom}\, E),$ or equivalently,
$(\mathrm{ker}(I-E), \mathrm{ker}\, E, \mathrm{mul} \, E).$ The relations
satisfying the inclusions $E^2 \subseteq E$ (sub-idempotent) or $E \subseteq
E^2$ (super-idempotent) play an important role. Lastly, the adjoint and the
closure of an idempotent linear relation are studied.


title: Existence and obstructions for the curvature on manifolds with boundary

abstract:
  We consider the problem of studying the set of curvature functions which a
given compact and non-compact manifold with nonempty boundary can possess.
First we prove that the sign demanded by the Gauss-Bonnet Theorem is a
necessary and sufficient condition for a given function to be the geodesic
curvature or the Gaussian curvature of some conformally equivalent metric. Our
proof conceptually differs from Cruz-Vit\'orio's proof since our approach allow
us to solve problems where the conformal method cannot solve. Also, we prove
new existence and nonexistence of metrics with prescribed curvature in the
conformal setting which depends on the Euler characteristic. After this, we
present a higher order analogue concerning scalar and mean curvatures on
compact manifolds with boundary. We also give conditions for Riemannian
manifolds not necessarily complete or compact to admit positive scalar
curvature and minimal boundary, without any auxiliary assumptions about its
infinity, which is an extension of those proved by Carlotto-Li


title:
Risk-based regulation for all: The need and a method for a wide adoption
  solution for data-driven inspection targeting

abstract:
  Access to data and data processing, including the use of machine learning
techniques, has become significantly easier and cheaper in recent years.
Nevertheless, solutions that can be widely adopted by regulators for market
monitoring and inspection targeting in a data-driven way have not been
frequently discussed by the scientific community. This article discusses the
need and the difficulties for the development of such solutions, presents an
effective method to address regulation planning, and illustrates its use to
account for the most important and common subject for the majority of
regulators: the consumer. This article hopes to contribute to increase the
awareness of the regulatory community to the need for data processing methods
that are objective, impartial, transparent, explainable, simple to implement
and with low computational cost, aiming to the implementation of risk-based
regulation in the world.


title: Pseudo Numerical Ranges and Spectral Enclosures

abstract:
  We introduce the new concepts of pseu\-do numerical range for operator
functions and families of sesquilinear forms as well as the pseu\-do block
numerical range for $n \times n$ operator matrix functions. While these notions
are new even in the bounded case, we cover operator polynomials with unbounded
coefficients, unbounded holomorphic form families of type (a) and associated
operator families of type (B). Our main results include spectral inclusion
properties of pseudo numerical ranges and pseudo block numerical ranges. For
diagonally dominant and off-diagonally dominant operator matrices they allow us
to prove spectral enclosures in terms of the pseudo numerical ranges of Schur
complements that no longer require dominance order $0$ and not even $<1$. As an
application, we establish a new type of spectral bounds for linearly damped
wave equations with possibly unbounded and/or singular damping.


title:
MP2-F12 basis set convergence near the complete basis set limit: are $h$
  functions sufficient?

abstract:
  We have investigated the title question for the W4-08 thermochemical
benchmark using partial-wave truncations of a large reference (REF) basis set,
as well as for standard F12-optimized basis sets. With the REF basis set, the
root mean square (RMS) contribution of i functions to the total atomization
energies (TAEs) is about 0.01 kcal/mol, the largest individual contributions
being 0.04 kcal/mol for \ce{P2} and \ce{P4}. However, even for these cases,
basis set extrapolation from \{g,h\} basis sets adequately addresses the
problem. Using basis sets insufficiently saturated in the $spdfgh$ angular
momenta may lead to exaggerated $i$ function contributions. For extrapolation
from $spdfg$ and $spdfgh$ basis sets, basis set convergence appears to be quite
close to the theoretical asymptotic $\propto L^{-7}$ behavior. We hence
conclude that $h$ functions are sufficient even for highly demanding F12
applications. With one-parameter extrapolation, $spdf$ and $spdfg$ basis sets
are adequate, with aug-cc-pV\{T,Q\}Z-F12 yielding RMSD=0.03 kcal/mol.


title: The combinator ${\bf M}$ and the Mockingbird lattice

abstract:
  We study combinatorial and order theoretic structures arising from the
fragment of combinatory logic spanned by the basic combinator ${\bf M}$. This
basic combinator, named as the Mockingbird by Smullyan, is defined by the
rewrite rule ${\bf M} x_1 \to x_1 x_1$. We prove that the reflexive and
transitive closure of this rewrite relation is a partial order on terms on
${\bf M}$ and that all connected components of its rewrite graph are Hasse
diagram of lattices. This last result is based on the introduction of new
lattices on duplicative forests, which are sorts of treelike structures. These
lattices are not graded, not self-dual, and not semidistributive. We present
some enumerative properties of these lattices like the enumeration of their
elements, of the edges of their Hasse diagrams, and of their intervals. These
results are derived from formal power series on terms and on duplicative
forests endowed with particular operations.


title: On maximally mixed equilibria of two-dimensional perfect fluids

abstract:
  The vorticity of a two-dimensional perfect (incompressible and inviscid)
fluid is transported by its area preserving flow. Given an initial vorticity
distribution $\omega_0$, predicting the long time behavior which can persist is
an issue of fundamental importance. In the infinite time limit, some
irreversible mixing of $\omega_0$ can occur. Since kinetic energy $\mathsf{E}$
is conserved, not all the mixed states are relevant and it is natural to
consider only the ones with energy $\mathsf{E}_0$ corresponding to $\omega_0$.
The set of said vorticity fields, denoted by
$\overline{\mathcal{O}_{\omega_0}}^*\cap \{ {\mathsf E}= {\mathsf E}_0\}$,
contains all the possible end states of the fluid motion. A. Shnirelman
introduced the concept of maximally mixed states (any further mixing would
necessarily change their energy), and proved they are perfect fluid equilibria.
We offer a new perspective on this theory by showing that any minimizer of any
strictly convex Casimir in $\overline{\mathcal{O}_{\omega_0}}^*\cap \{ {\mathsf
E}= {\mathsf E}_0\}$ is maximally mixed, as well as discuss its relation to
classical statistical hydrodynamics theories. Thus, (weak) convergence to
equilibrium cannot be excluded solely on the grounds of vorticity transport and
conservation of kinetic energy. On the other hand, on domains with symmetry
(e.g. straight channel or annulus), we exploit all the conserved quantities and
the characterizations of $\overline{\mathcal{O}_{\omega_0}}^*\cap \{ {\mathsf
E}= {\mathsf E}_0\}$ to give examples of open sets of initial data which can be
arbitrarily close to any shear or radial flow in $L^1$ of vorticity but do not
weakly converge to them in the long time limit.


title: Music Influence Modeling Based on Directed Network Model

abstract:
  Studying the history of music may provide a glimpse into the development of
human creativity as we examine the evolutionary and revolutionary trends in
music and genres. First, a musical influence metric was created to construct a
directed network of musical influence. Second, we examined the revolutions and
development of musical genres, modeled the similarity, and explored
similarities and influences within and between genres. Hierarchical cluster
analysis and time series analysis of genres were used to explore the
correlation between genres. Finally, Network Analysis, Semantic Analysis, and
Random Forest Model are employed to find the revolutionaries.
  The above work was applied to Country music to sort out and analyze its
evolution. In studying the connection between music and the social environment,
time series analysis is used to determine the impact of social, political, or
technological changes on music.


title: Collecting, Classifying, Analyzing, and Using Real-World Elections

abstract:
  We present a collection of $7582$ real-world elections divided into $25$
datasets from various sources ranging from sports competitions over music
charts to survey- and indicator-based rankings. We provide evidence that the
collected elections complement already publicly available data from the PrefLib
database, which is currently the biggest and most prominent source containing
$701$ real-world elections from $36$ datasets. Using the map of elections
framework, we divide the datasets into three categories and conduct an analysis
of the nature of our elections. To evaluate the practical applicability of
previous theoretical research on (parameterized) algorithms and to gain further
insights into the collected elections, we analyze different structural
properties of our elections including the level of agreement between voters and
election's distances from restricted domains such as single-peakedness. Lastly,
we use our diverse set of collected elections to shed some further light on
several traditional questions from social choice, for instance, on the number
of occurrences of the Condorcet paradox and on the consensus among different
voting rules.


title:
Reliability of the Ginzburg-Landau Theory in the BCS-BEC Crossover by
  Including Gaussian Fluctuations for 3D Attractive Fermions

abstract:
  We calculate the parameters of the Ginzburg-Landau (GL) equation of a
three-dimensional attractive Fermi gas around the superfluid critical
temperature. We compare different levels of approximation throughout the
Bardeen-Cooper-Schrieffer (BCS) to the Bose-Einstein Condensate (BEC) regime.
We show that the inclusion of Gaussian fluctuations strongly modifies the
values of the Ginzburg-Landau parameters approaching the BEC regime of the
crossover. We investigate the reliability of the Ginzburg-Landau theory, with
fluctuations, studying the behavior of the coherence length and of the critical
rotational frequencies throughout the BCS-BEC crossover. The effect of the
Gaussian fluctuations gives qualitative correct trends of the considered
physical quantities from the BCS regime up to the unitary limit of the BCS-BEC
crossover. Approaching the BEC regime, the Ginzburg-Landau equation with the
inclusion of Gaussian fluctuations turns out to be unreliable.


title:
Using electrical resistance asymmetries to infer the geometric shapes of
  foundry patterned nanophotonic structures

abstract:
  While silicon photonics has leveraged the nanofabrication tools and
techniques from the microelectronics industry, it has also inherited the
metrological methods from the same. Photonics fabrication is inherently
different from microelectronics in its intrinsic sensitivity to 3D shape and
geometry, especially in a high-index contrast platform like
silicon-on-insulator. In this work, we show that electrical resistance
measurements can in principle be used to infer the geometry of such
nanophotonic structures and reconstruct the micro-loading curves of foundry
etch processes. We implement our ideas to infer 3D geometries from a standard
silicon photonics foundry and discuss some of the potential sources of error
that need to be calibrated out to improve the reconstruction accuracy.


title:
Testing the limits of natural language models for predicting human
  language judgments

abstract:
  Neural network language models can serve as computational hypotheses about
how humans process language. We compared the model-human consistency of diverse
language models using a novel experimental approach: controversial sentence
pairs. For each controversial sentence pair, two language models disagree about
which sentence is more likely to occur in natural text. Considering nine
language models (including n-gram, recurrent neural networks, and transformer
models), we created hundreds of such controversial sentence pairs by either
selecting sentences from a corpus or synthetically optimizing sentence pairs to
be highly controversial. Human subjects then provided judgments indicating for
each pair which of the two sentences is more likely. Controversial sentence
pairs proved highly effective at revealing model failures and identifying
models that aligned most closely with human judgments. The most
human-consistent model tested was GPT-2, although experiments also revealed
significant shortcomings of its alignment with human perception.


title: AutoRF: Learning 3D Object Radiance Fields from Single View Observations

abstract:
  We introduce AutoRF - a new approach for learning neural 3D object
representations where each object in the training set is observed by only a
single view. This setting is in stark contrast to the majority of existing
works that leverage multiple views of the same object, employ explicit priors
during training, or require pixel-perfect annotations. To address this
challenging setting, we propose to learn a normalized, object-centric
representation whose embedding describes and disentangles shape, appearance,
and pose. Each encoding provides well-generalizable, compact information about
the object of interest, which is decoded in a single-shot into a new target
view, thus enabling novel view synthesis. We further improve the reconstruction
quality by optimizing shape and appearance codes at test time by fitting the
representation tightly to the input image. In a series of experiments, we show
that our method generalizes well to unseen objects, even across different
datasets of challenging real-world street scenes such as nuScenes, KITTI, and
Mapillary Metropolis.


title: Heterogeneous Target Speech Separation

abstract:
  We introduce a new paradigm for single-channel target source separation where
the sources of interest can be distinguished using non-mutually exclusive
concepts (e.g., loudness, gender, language, spatial location, etc). Our
proposed heterogeneous separation framework can seamlessly leverage datasets
with large distribution shifts and learn cross-domain representations under a
variety of concepts used as conditioning. Our experiments show that training
separation models with heterogeneous conditions facilitates the generalization
to new concepts with unseen out-of-domain data while also performing
substantially higher than single-domain specialist models. Notably, such
training leads to more robust learning of new harder source separation
discriminative concepts and can yield improvements over permutation invariant
training with oracle source selection. We analyze the intrinsic behavior of
source separation training with heterogeneous metadata and propose ways to
alleviate emerging problems with challenging separation conditions. We release
the collection of preparation recipes for all datasets used to further promote
research towards this challenging task.


title: Markovianity and the Thompson Group $F$

abstract:
  We show that representations of the Thompson group $F$ in the automorphisms
of a noncommutative probability space yield a large class of bilateral
stationary noncommutative Markov processes. As a partial converse, bilateral
stationary Markov processes in tensor dilation form yield representations of
$F$. As an application, and building on a result of K\"ummerer, we canonically
associate a representation of $F$ to a bilateral stationary Markov process in
classical probability.


title: Controlling Golog Programs against MTL Constraints

abstract:
  While Golog is an expressive programming language to control the high-level
behavior of a robot, it is often tedious to use on a real robotic system. On an
actual robot, the user needs to consider low-level details, such as enabling
and disabling hardware components, e.g., a camera to detect objects for
grasping. In other words, high-level actions usually pose implicit temporal
constraints on the low-level platform, which are typically independent of the
concrete program to be executed. In this paper, we propose to make these
constraints explicit by modeling them as MTL formulas, which enforce the
execution of certain low-level platform operations in addition to the main
program. Based on results from timed automata controller synthesis, we describe
a method to synthesize a controller that executes both the high-level program
and the low-level platform operations concurrently in order to satisfy the MTL
specification. This allows the user to focus on the high-level behavior without
the need to consider low-level operations. We present an extension to Golog by
clocks together with the required theoretical foundations as well as
decidability results.


title:
Imitating, Fast and Slow: Robust learning from demonstrations via
  decision-time planning

abstract:
  The goal of imitation learning is to mimic expert behavior from
demonstrations, without access to an explicit reward signal. A popular class of
approach infers the (unknown) reward function via inverse reinforcement
learning (IRL) followed by maximizing this reward function via reinforcement
learning (RL). The policies learned via these approaches are however very
brittle in practice and deteriorate quickly even with small test-time
perturbations due to compounding errors. We propose Imitation with Planning at
Test-time (IMPLANT), a new meta-algorithm for imitation learning that utilizes
decision-time planning to correct for compounding errors of any base imitation
policy. In contrast to existing approaches, we retain both the imitation policy
and the rewards model at decision-time, thereby benefiting from the learning
signal of the two components. Empirically, we demonstrate that IMPLANT
significantly outperforms benchmark imitation learning approaches on standard
control environments and excels at zero-shot generalization when subject to
challenging perturbations in test-time dynamics.


title: Global polarization and spin alignment measurements

abstract:
  Recent experimental results on global polarization of hyperons and spin
alignment of vector mesons in heavy-ion collisions are reviewed, especially in
context of the energy dependence and particle species dependence. New results
on local polarization along the beam direction at the LHC are discussed with
previous measurements at RHIC. Future outlook of these measurements and
possible future directions for better understanding of local vorticity field
are briefly discussed.


title:
Axion Searches at the LHC: FASER as a Light Shining through Walls
  Experiment

abstract:
  We propose the use of FASER as a light-shining-through-walls experiment to
search for axions. LHC collisions generate a high intensity and high energy
photon flux in the forward direction which can oscillate into axions in the
magnetic fields that are used to confine the beam. These axions then pass
through about 100~m of rock before reaching the magnetic fields of FASER, where
they can convert back into photons and be detected by an electromagnetic
calorimeter. In the next years, FASER and its successor FASER2 at the Forward
Physics Facility will be able to explore regions of the axion parameter space
inaccessible by other laboratory-based experiments.


title: The twining character formula for reductive groups

abstract:
  Let $\widehat{G}$ be a connected reductive group over an algebraically closed
field with a pinning-preserving outer automorphism $\sigma$. Jantzen's twining
character formula relates the trace of the action of $\sigma$ on a
highest-weight representation $V_{\mu}$ of $\widehat{G}$ to the character of a
corresponding highest-weight representation $(V_{\sigma})_{\mu}$ of a related
group $\widehat{G^{\sigma, \circ}}$. This paper extends the methods of Hong's
geometric proof for the case $\widehat{G}$ is adjoint, to prove that the
formula holds for all connected reductive groups, and examines the role of
additional hypotheses. In the final section, it is explained how these results
can be used to draw conclusions about quasi-split groups over a non-Archimedean
local field. This paper thus provides a more general geometric proof of the
Jantzen twining character formula and provides some apparently new results of
independent interest along the way.


title: Entanglement entropy and negativity in the Ising model with defects

abstract:
  Defects in two-dimensional conformal field theories (CFTs) contain signatures
of their characteristics. In this work, we compute the entanglement entropy
(EE) and the entanglement negativity (EN) of subsystems in the presence of
energy and duality defects in the Ising CFT using the density matrix
renormalization group (DMRG) technique. We show that the EE for the duality
defect exhibits fundamentally different characteristics compared to the energy
defect due to the existence of localized and delocalized zero energy modes. Of
special interest is the nontrivial `finite-size correction' in the EE obtained
recently using free fermion computations. These corrections arise when the
subsystem size is appreciable compared to the total system size and lead to a
deviation from the usual logarithmic scaling characteristic of one-dimensional
quantum-critical systems. Using matrix product states with open and infinite
boundary conditions, we numerically demonstrate the disappearance of the zero
mode contribution for finite subsystem sizes in the thermodynamic limit. Our
results provide further support to the recent free fermion computations, but
clearly contradict earlier analytical field theory calculations based on
twisted torus partition functions. Subsequently, we compute the logarithm of
the EN (log-EN) between two disjoint subsystems separated by a defect. We show
that the log-EN scales logarithmically with the separation of the subsystems.
However, the coefficient of this logarithmic scaling yields a
continuously-varying effective central charge that is different from that
obtained from analogous computations of the EE. The defects leave their
fingerprints in the subleading term of the scaling of the log-EN. Furthermore,
the log-EN receives similar `finite size corrections' like the EE which leads
to deviations from its characteristic logarithmic scaling.

categories: hep-th cond-mat.stat-mech math-ph math.MP quant-ph


title: A General Class of Trimodal Distributions: Properties and Inference

abstract:
  The modality is important topic for modelling. Using parametric models is an
efficient way when real data set shows trimodality. In this paper we propose a
new class of trimodal probability distributions, that is, probability
distributions that have up to three modes. Trimodality itself is achieved by
applying a proper transformation to density function of certain continuous
probability distributions. At first, we obtain preliminary results for an
arbitratry density function $g(x)$ and, next, we focus on the Gaussian case,
studying trimodal Gaussian model more deeply. The Gaussian distribution is
applied to produce the trimodal form of Gaussian known as normal distribution.
The tractability of analytical expression of normal distribution, and
properties of the trimodal normal distribution are important reasons why we
choose normal distribution. Furthermore, the existing distributions should be
improved to be capable of modelling efficiently when there exists a trimodal
form in a data set. After new density function is proposed, estimating its
parameters is important. Since Mathematica 12.0 software has optimization tools
and important modelling techniques, computational steps are performed by using
this software. The bootstrapped form of real data sets are applied to show the
modelling ability of the proposed distribution when real data sets show
trimodality.


title:
Algebraic Structure of the Weak Stage Order Conditions for Runge-Kutta
  Methods

abstract:
  Runge-Kutta (RK) methods may exhibit order reduction when applied to stiff
problems. For linear problems with time-independent operators, order reduction
can be avoided if the method satisfies certain weak stage order (WSO)
conditions, which are less restrictive than traditional stage order conditions.
This paper outlines the first algebraic theory of WSO, and establishes general
order barriers that relate the WSO of a RK scheme to its order and number of
stages for both fully-implicit and DIRK schemes. It is shown in several
scenarios that the constructed bounds are sharp. The theory characterizes WSO
in terms of orthogonal invariant subspaces and associated minimal polynomials.
The resulting necessary conditions on the structure of RK methods with WSO are
then shown to be of practical use for the construction of such schemes.


title: Unidirectional ultracompact DNA-templated optical antennas

abstract:
  Optical nanoantennas are structures designed to manipulate light-matter
interactions at the nanoscale by interfacing propagating light with localized
optical fields. In recent years, a plethora of devices have been realized that
are able to efficiently tailor the absorption and/or emission rates of
fluorophores. By contrast, modifying the spatial characteristics of their
radiation fields remains a challenge. Up to date, the designs providing
directionality to fluorescence emission have required compound, complex
geometries with overall dimensions comparable to the operating wavelength.
Here, we present the fabrication and characterization of DNA-templated
ultracompact optical antennas, with sub-wavelength sizes and capable of
directing single-molecule fluorescence into predefined directions. Using the
DNA origami methodology, two gold nanorods are assembled side-to-side with a
separation gap of 5 nm. We show that a single fluorescent molecule placed at
the tip of one of the nanorods drives the dimer antenna in anti-phase, leading
to unidirectional emission.


title:
Possible signature of broken symmetry state near the quantum critical
  point in P doped BaFe$_2$As$_2$: A Raman spectroscopy study

abstract:
  We study the iron-pnictide compound BaFe$_2$(As$_{1-x}$P$_x$)$_2$ for x
$\sim$0.23, with a doping concentration near quantum criticality and enhanced
nematic fluctuating state in the doping-temperature phase diagram. Transport
measurements confirm the presence of a magneto-structural transition at 60K
from the tetragonal to the orthorhombic phase, followed by a superconducting
transition below 16K. The temperature and polarisation dependent Raman spectra
reveal that there is a phonon mode at 211 cm$^{-1}$, followed by two broad
modes (BM) between 400 and 700 cm$^{-1}$, having an energy difference of 15
meV, in the temperature range between 300K and 80K. In the non-superconducting
state, the phonon mode exhibits expected polarization dependence as well as
temperature evolution due to anharmonicity, strong anisotropic and thermally
inert behaviour are observed for the BM. Electronic structure calculations for
doped and undoped BaFe$_2$As$_2$ show that while Fe $d_{xz}$ and $d_{yz}$
orbitals do not split in the tetragonal phase, the splitting energy is 13.5 meV
in the orthorhombic phase of the doped system, which is reasonably close to the
experimentally observed value of the energy separation of the BM. We believe
that reported BM possibly are the signature of electronic Raman scattering
involving the crystal field levels of $d$-orbitals of Fe$^{2+}$ due to local
breaking of the C$_4$ symmetry of the parent compound in the doped system.


title:
Thermal Equilibrium Solutions of Black Hole Accretion Flows: Outflows VS
  Advection

abstract:
  Observations and numerical simulations have shown that outflows generally
exist in the accretion process. We revisit the thermal equilibrium solutions of
black hole accretion flows by including the role of outflows. Our study focuses
on the comparison of the cooling rate of outflows with that of advection. Our
results show that, except for the inner region, outflows can dominate over
advection in a wide range of the flow, which is in good agreement with previous
numerical simulations. We argue that an advection-dominated inner region
together with an outflow-dominated outer region should be a general radial
distribution for both super-Eddington accretion flows and optically thin flows
with low accretion rates.


title: Rigidity Theorems for Asymptotically Euclidean $Q$-singular Spaces

abstract:
  In this paper we prove some rigidity theorems associated to $Q$-curvature
analysis on asymptotically Euclidean (AE) manifolds, which are inspired by the
analysis of conservation principles within fourth order gravitational theories.
A central object in this analysis is a notion of fourth order energy,
previously analysed by the authors, which is subject to a positive energy
theorem. We show that this energy can be more geometrically rewritten in terms
of a fourth order analogue to the Ricci tensor, which we denote by $J_g$. This
allows us to prove that Yamabe positive $J$-flat AE manifolds must be isometric
to Euclidean space. As a by product, we prove that this $J$-tensor provides a
geometric control for the optimal decay rates at infinity. This last result
reinforces the analogy of $J$ as a fourth order analogue to the Ricci tensor.


title:
Influence of light quark loops on the Wigner phase with Dyson-Schwinger
  equations approach

abstract:
  We study the influence of light quark loops on the Wigner phase by solving
coupled Dyson-Schwinger equations for quark propagator and gluon propagator. We
take the gluon propagator in the Nambu phase from $N_f$ = 2 unquenched lattice
QCD and choose various phenomenological models for the quark-gluon vertex. The
gluon propagator in Winger phase is assumed to be different from that in the
Nambu phase only due to the vacuum polarization of the quark loop. We obtain
the Wigner solution of the coupled equations, compared with that from solving
only the equation of the quark propagator. We discussed the corrections by the
light quark loops and the dependence on various models of the quark-gluon
vertex.


title: Pin the Memory: Learning to Generalize Semantic Segmentation

abstract:
  The rise of deep neural networks has led to several breakthroughs for
semantic segmentation. In spite of this, a model trained on source domain often
fails to work properly in new challenging domains, that is directly concerned
with the generalization capability of the model. In this paper, we present a
novel memory-guided domain generalization method for semantic segmentation
based on meta-learning framework. Especially, our method abstracts the
conceptual knowledge of semantic classes into categorical memory which is
constant beyond the domains. Upon the meta-learning concept, we repeatedly
train memory-guided networks and simulate virtual test to 1) learn how to
memorize a domain-agnostic and distinct information of classes and 2) offer an
externally settled memory as a class-guidance to reduce the ambiguity of
representation in the test data of arbitrary unseen domain. To this end, we
also propose memory divergence and feature cohesion losses, which encourage to
learn memory reading and update processes for category-aware domain
generalization. Extensive experiments for semantic segmentation demonstrate the
superior generalization capability of our method over state-of-the-art works on
various benchmarks.


title: Unified Contrastive Learning in Image-Text-Label Space

abstract:
  Visual recognition is recently learned via either supervised learning on
human-annotated image-label data or language-image contrastive learning with
webly-crawled image-text pairs. While supervised learning may result in a more
discriminative representation, language-image pretraining shows unprecedented
zero-shot recognition capability, largely due to the different properties of
data sources and learning objectives. In this work, we introduce a new
formulation by combining the two data sources into a common image-text-label
space. In this space, we propose a new learning paradigm, called Unified
Contrastive Learning (UniCL) with a single learning objective to seamlessly
prompt the synergy of two data types. Extensive experiments show that our UniCL
is an effective way of learning semantically rich yet discriminative
representations, universally for image recognition in zero-shot, linear-probe,
fully finetuning and transfer learning scenarios. Particularly, it attains
gains up to 9.2% and 14.5% in average on zero-shot recognition benchmarks over
the language-image contrastive learning and supervised learning methods,
respectively. In linear probe setting, it also boosts the performance over the
two methods by 7.3% and 3.4%, respectively. Our study also indicates that UniCL
stand-alone is a good learner on pure image-label data, rivaling the supervised
learning methods across three image classification datasets and two types of
vision backbones, ResNet and Swin Transformer. Code is available at
https://github.com/microsoft/UniCL.


title:
Density functional approach to quark matter with confinement and color
  superconductivity

abstract:
  We present a novel relativistic density-functional approach to modeling quark
matter with a mechanism to mimic confinement. The quasiparticle treatment of
quarks provides their suppression due to a large quark selfenergy already at
the mean-field level. We demonstrate that our approach is equivalent to a
chiral quark model with medium-dependent couplings. The dynamical restoration
of the chiral symmetry is ensured by construction of the density functional.
Supplemented with the vector repulsion and diquark pairing the model is applied
to construct a hybrid quark-hadron EoS of cold compact-star matter. We study
the connection of such a hybrid EoS with the stellar mass-radius relation and
tidal deformability. The model results are compared to various observational
constraints including the NICER radius measurement of PSR J0740+6620 and the
tidal deformability constraint from GW170817. The model is shown to be
consistent with the constraints, still allowing for further improvement by
adjusting the vector repulsion and diquark pairing couplings.


title: Topologically protected vortex knots and links

abstract:
  We propose a class of tangled vortex structures, tied from non-Abelian
topological vortices, which are immune against decaying through local
reconnections and strand crossings that are allowed by the system. We refer to
such structures as being topologically protected. We then turn our attention to
topological vortices classified by the quaternion group $Q_8$ ($Q_8$-colored
links), which are realizable in systems consisting either of the biaxial
nematic or the cyclic phase of a spin-2 Bose--Einstein condensate, or of
biaxial nematic liquid crystal, and prove the existence of topologically
protected $Q_8$-colored links. Remarkably, the strongest invariant we
construct, the $Q$-invariant of $Q_8$-colored links, can be used to classify
$Q_8$-colored links up to allowed local surgeries on the vortex cores.


title: Topological D-Class Physics with Passive Acoustic Elements

abstract:
  In this work, we supply an engineering solution that paves the way to the
implementation of the full classification table of topological insulators with
passive acoustic crystals. As an example, we design an acoustic crystal
displaying the full range of characteristics of a topological insulator from
class D, such as non-trivial Chern numbers and an exact particle-hole symmetry
for both bulk and edge dynamics. The key is a particular geometry of the
acoustic resonators that facilitates a large number of independent and
equal-strength couplings, eight to be precise, such that the mode-coupling
theory remains accurate. The latter enables us to implement with high fidelity
the de-complexified tight-binding model generating the topological phases from
class D in 2-dimensions, previously announced in [Barlas et al, PRB (2018)].
The successful realization of a topological acoustic crystal from class D paves
the way to the observation of the Majorana-like modes stabilized by pi-fluxes.


title:
Impact of calibration uncertainties on Hubble constant measurements from
  gravitational-wave sources

abstract:
  Gravitational-wave (GW) detections of electromagnetically bright compact
binary coalescences can provide an independent measurement of the Hubble
constant $H_0$. In order to obtain a measurement that could help arbitrating
the existing tension on $H_0$, one needs to fully understand any source of
systematic biases for this approach. In this study, we aim at understanding the
impact of instrumental calibration errors (CEs) and uncertainties on luminosity
distance measurements, $D_L$, and the inferred $H_0$ results. We simulate
binary neutron star mergers (BNSs), as detected by a network of Advanced LIGO
and Advanced Virgo interferometers at their design sensitivity. We artificially
add CEs equal to exceptionally large values experienced in LIGO-Virgo's third
observing run (O3). We find that for individual BNSs at a network
signal-to-noise ratio of 50, the systematic errors on $D_L$ - and hence $H_0$ -
are still smaller than the statistical uncertainties. The biases become more
significant when we combine multiple events to obtain a joint posterior on
$H_0$. In the rather unrealistic case that the data around each detection is
affected by the same CEs corresponding to the worst offender of O3, the true
$H_0$ value would be excluded from the 90% credible interval after $\sim40$
sources. If instead 10% of the sources suffer from severe CEs, the true value
of $H_0$ is included in the 90% credible interval even after we combine 100
sources.


title: Group actions on simple tracially $\mathcal{Z}$-absorbing C*-algebras

abstract:
  We show that if $A$ is a simple (not necessarily unital) tracially
$\mathcal{Z}$-absorbing C*-algebra and $\alpha \colon G \to \mathrm{Aut} (A)$
is an action of a finite group $G$ on $A$ with the weak tracial Rokhlin
property, then the crossed product $C^*(G, A,\alpha)$ and the fixed point
algebra $A^\alpha$ are simple and tracially $\mathcal{Z}$-absorbing, and they
are $\mathcal{Z}$-stable if, in addition, $A$ is separable and nuclear. The
same conclusion holds for all intermediate C*-algebras of the inclusions
$A^\alpha \subseteq A$ and $A \subseteq C^*(G, A,\alpha)$. We prove that if $A$
is a simple tracially $\mathcal{Z}$-absorbing C*-algebra, then, under a
finiteness condition, the permutation action of the symmetric group $S_m$ on
the minimal $m$-fold tensor product of $A$ has the weak tracial Rokhlin
property. We define the weak tracial Rokhlin property for automorphisms of
simple C*-algebras and we show that -- under a mild assumption -- (tracial)
$\mathcal{Z}$-absorption is preserved under crossed products by such
automorphisms.


title:
Dissolving the Segmentation of Shared Mobility Markets: A Unified
  Theoretical Framework and Four Examples

abstract:
  In mobility sharing markets, there are two conflicting principles: 1) the
healthy competition between platforms, such as Uber and Lyft, and 2) economies
of network scale, which leads to higher chances for trips to be matched, and
thus higher efficiency. The status quo mobility sharing markets are either
monopolistic or largely segmented with significant efficiency loss. In this
paper, we first introduce a unified theoretical framework to describe the
shared mobility market, and then use it to propose four market structure
designs to dissolve the market segmentation to different extents. Using a
formal analysis framework, we analyze the following aspects of the two status
quo and four proposed market structures: demand and supply information
collection, service delivery and payment flow. For each proposed market,
detailed mechanisms are introduced and feasibility for market realization is
also discussed.


title: On the origin of matter-antimatter asymmetry in the Universe

abstract:
  In order to investigate the origin of matter-antimatter asymmetry in the
Universe, we adopt a theoretical framework where the standard model emerges as
a Poincar\'e invariant field theory localized at a domain-antidomain wall
(DW-aDW) brane pair of a theory which lives on a higher dimensional bulk. We
argue that such a system of a parallel DW-aDW pair could have been created at a
very early epoch of the cosmological evolution when the Universe was still of
microscopic size because its creation is topologically possible as compared to
a single DW creation when the perpendicular extra dimension is compact. The
conservation laws, such as of charge and chirality, are not violated in vacuum
fluctuations of the combined DW-aDW system, but as we show their simultaneous
conservation for each wall separately may not be favorable in certain
processes. In particular, as expansion of spacetime occurs in the higher
dimensional bulk, the distance d between the DW and the aDW increases as a
function of time. We show that during the early stages of the cosmological
evolution, when d was of microscopic size, the leading mechanism for
pair-creation from fluctuations of the gauge-field with polarization
perpendicular to the DWs (i.e., polarization along the extra dimension) was one
in which the particle and the antiparticle were created on the opposite
domain-walls hosting opposite chirality fermions. This mechanism allows for a
matter-antimatter asymmetry to appear separately in the DW and the aDW, while
in the combined DW-aDW system no such asymmetry was allowed. In this scenario,
at a later and the present stage of the cosmological evolution, where d is
macroscopically large, the probability to violate these conservation laws on a
single DW, as a function of the inter-wall distance, is exponentially
suppressed.


title: Pneumonia Detection in Chest X-Rays using Neural Networks

abstract:
  With the advancement in AI, deep learning techniques are widely used to
design robust classification models in several areas such as medical diagnosis
tasks in which it achieves good performance. In this paper, we have proposed
the CNN model (Convolutional Neural Network) for the classification of Chest
X-ray images for Radiological Society of North America Pneumonia (RSNA)
datasets. The study also tries to achieve the same RSNA benchmark results using
the limited computational resources by trying out various approaches to the
methodologies that have been implemented in recent years. The proposed method
is based on a non-complex CNN and the use of transfer learning algorithms like
Xception, InceptionV3/V4, EfficientNetB7. Along with this, the study also tries
to achieve the same RSNA benchmark results using the limited computational
resources by trying out various approaches to the methodologies that have been
implemented in recent years. The RSNA benchmark MAP score is 0.25, but using
the Mask RCNN model on a stratified sample of 3017 along with image
augmentation gave a MAP score of 0.15. Meanwhile, the YoloV3 without any
hyperparameter tuning gave the MAP score of 0.32 but still, the loss keeps
decreasing. Running the model for a greater number of iterations can give
better results.


title:
Modeling Label Correlations for Second-Order Semantic Dependency Parsing
  with Mean-Field Inference

abstract:
  Second-order semantic parsing with end-to-end mean-field inference has been
shown good performance. In this work we aim to improve this method by modeling
label correlations between adjacent arcs. However, direct modeling leads to
memory explosion because second-order score tensors have sizes of $O(n^3L^2)$
($n$ is the sentence length and $L$ is the number of labels), which is not
affordable. To tackle this computational challenge, we leverage tensor
decomposition techniques, and interestingly, we show that the large
second-order score tensors have no need to be materialized during mean-field
inference, thereby reducing the computational complexity from cubic to
quadratic. We conduct experiments on SemEval 2015 Task 18 English datasets,
showing the effectiveness of modeling label correlations. Our code is publicly
available at https://github.com/sustcsonglin/mean-field-dep-parsing.


title:
An Online Learning Approach to Shortest Path and Backpressure Routing in
  Wireless Networks

abstract:
  We consider the adaptive routing problem in multihop wireless networks. The
link states are assumed to be random variables drawn from unknown
distributions, independent and identically distributed across links and time.
This model has attracted a growing interest recently in cognitive radio
networks and adaptive communication systems. In such networks, devices are
cognitive in the sense of learning the link states and updating the
transmission parameters to allow efficient resource utilization. This model
contrasts sharply with the vast literature on routing algorithms that assumed
complete knowledge about the link state means. The goal is to design an
algorithm that learns online optimal paths for data transmissions to maximize
the network throughput while attaining low path cost over flows in the network.
We develop a novel Online Learning for Shortest path and Backpressure (OLSB)
algorithm to achieve this goal. We analyze the performance of OLSB rigorously
and show that it achieves a logarithmic regret with time, defined as the loss
of an algorithm as compared to a genie that has complete knowledge about the
link state means. We further evaluate the performance of OLSB numerically via
extensive simulations, which support the theoretical findings and demonstrate
its high efficiency.


title: Misalignments in NGC 1068

abstract:
  NGC 1068 is a nearby Active Galactic Nucleus (AGN) of type 2, meaning that
its accretion disk is hidden behind a large amount of foreground extinction.
Observations at several wavelengths have revealed various disk-like structures
around the nucleus, all possibly part of the putative torus responsible for the
obscuration of the AGN. The paper presents results based on GRAVITY/VLTI
interferometric observations in the near-infrared, which provide very high
angular resolution, and gives insights into the geometry of the innermost
region of the torus. The 3D orientation of the structure is surprising in
several aspects, as it is misaligned with other disks present around the
nucleus, and leaves a clear line of sight toward the central source.


title:
Fractal dimension for a class of complex-valued fractal interpolation
  functions

abstract:
  There are many research papers dealing with fractal dimension of real-valued
fractal functions in the recent literature. The main focus of the present paper
is to study fractal dimension of complex-valued functions. This paper also
highlights the difference between dimensional results of the complex-valued and
real-valued fractal functions. In this paper, we study the fractal dimension of
the graph of complex-valued function $g(x)+i h(x)$, compare its fractal
dimension with the graphs of functions $g(x)+h(x)$ and $(g(x),h(x))$ and also
obtain some bounds. Moreover, we study the fractal dimension of the graph of
complex-valued fractal interpolation function associated with a germ function
$f$, base function $b$ and scaling functions $\alpha_k$.


title: Real adjoint orbits of the unipotent subgroup

abstract:
  Let $G$ be a linear Lie group that acts on it's Lie algebra $\mathfrak{g}$ by
the adjoint action: $\mathrm{Ad}(g)X=gXg^{-1}$. An element $X\in \mathfrak {g}$
is called $\mathrm{Ad}_G$-real if $-X = \mathrm{Ad}(g)X $ for some $g\in G$. An
$\mathrm{Ad}_G$-real element $X$ is called strongly $\mathrm{Ad}_G $-real if
$-X = \mathrm{Ad}(\tau) X $ for some involution $\tau\in G$.
  Let $K=\mathbb{R}$, $\mathbb{C}$ or $\mathbb{H}$. Let $\mathrm{U}_n(K)$ be
the group of unipotent upper-triangular matrices over $K$. Let $\mathfrak{u}_n
(K)$ be the Lie algebra of $\mathrm{U}_n(K)$ that consists of $n \times n$
upper triangular matrices with $0$ in all the diagonal entries. In this paper,
we consider the $\mathrm{Ad}$-reality of the Lie algebra $ \mathfrak{u}_n(K) $
that comes from the adjoint action of the Lie group $\mathrm{U}_n(K)$ on $
\mathfrak{u}_n(K)$. We prove that there is no non-trivial
$\mathrm{Ad}_{\mathrm{ U}_n(K)}$-real element in $\mathfrak{u}_n (K)$. We also
consider the adjoint action of the extended group $\mathrm{U}_n^\pm(K)$ that
consists of all upper triangular matrices over $K$ having diagonal elements as
$1$ or $-1$, and construct a large class of $\mathrm{Ad} _{\mathrm{ U}_n^\pm(
K)} $-real elements. As applications of these results, we recover related
results concerning classical reality in these groups.


title: Real adjoint orbits of special linear groups

abstract:
  Let $ G $ be a Lie group with Lie algebra $ \mathfrak{g} $. An element $ X
\in \mathfrak{g} $ is called $\mathrm{Ad}_G$-real if $ -X=gXg^{-1} $ for some $
g \in G $. Moreover, if $ -X=gXg^{-1} $ holds for some involution $ g\in G $,
then $ X $ is called strongly $\mathrm{Ad}_G$-real. We have classified the
$\mathrm{Ad}_G$-real and the strongly $\mathrm{Ad}_G$-real orbits in the
special linear Lie algebra $\mathfrak{sl}(n,\mathbb{F}) $ for $
\mathbb{F}=\mathbb{C}$ or $\mathbb{H} $.


title:
Security Aspects of Quantum Machine Learning: Opportunities, Threats and
  Defenses

abstract:
  In the last few years, quantum computing has experienced a growth spurt. One
exciting avenue of quantum computing is quantum machine learning (QML) which
can exploit the high dimensional Hilbert space to learn richer representations
from limited data and thus can efficiently solve complex learning tasks.
Despite the increased interest in QML, there have not been many studies that
discuss the security aspects of QML. In this work, we explored the possible
future applications of QML in the hardware security domain. We also expose the
security vulnerabilities of QML and emerging attack models, and corresponding
countermeasures.


title: Global existence and pointwise decay for the null condition

abstract:
  We study the nonlinear wave equation with the null condition and small
initial data on nonstationary spacetimes. Under the assumption that the
solution to the linear equation satisfies local energy bounds, we prove global
existence of solutions, and obtain sharp pointwise bounds.


title:
Canonical partition function and center symmetry breaking in finite
  density lattice gauge theories

abstract:
  We study the nature of the phase transition of lattice gauge theories at high
temperature and high density by focusing on the probability distribution
function, which represents the probability that a certain density will be
realized in a heat bath. The probability distribution function is obtained by
creating a canonical partition function fixing the number of particles from the
grand partition function. However, if the Z_3 center symmetry, which is
important for understanding the finite temperature phase transition of SU(3)
lattice gauge theory, is maintained on a finite lattice, the probability
distribution function is always zero, except when the number of particles is a
multiple of 3. For U(1) lattice gauge theory, this problem is more serious. The
probability distribution becomes zero when the particle number is nonzero. This
problem is essentially the same as the problem that the expectation value of
the Polyakov loop is always zero when calculating with finite volume. In this
study, we propose a solution to this problem. We also propose a method to avoid
the sign problem, which is an important problem at finite density, using the
center symmetry. In the case of U(1) lattice gauge theory with heavy fermions,
numerical simulations are actually performed, and we demonstrate that the
probability distribution function at a finite density can be calculated by the
method proposed in this study. Furthermore, the application of this method to
QCD is discussed.


title:
High-throughput computational discovery of 40 ultralow thermal
  conductivity and 20 highly anisotropic crystalline materials

abstract:
  We performed ab-initio driven density functional theory-based high throughput
computations to search for materials with low thermal conductivity and high
thermal transport anisotropy. We shortlisted a pool of 429 stable ternary
semiconductors from the Materials Project and obtained phonon thermal
conductivity by solving the Boltzmann transport equation on 225 materials. We
found the lowest thermal conductivity of 0.16 W/m-K in SbRbK 2 and 40 materials
with a thermal conductivity lower than 1 W/m-K at 300 K. For anisotropic
thermal transport, we have identified six materials with anisotropy larger than
5 and 20 with thermal transport anisotropy higher than the largest reported
literature value.


title:
Cosmic rays and thermal instability in self-regulating cooling flows of
  massive galaxy clusters

abstract:
  One of the key physical processes that helps prevent strong cooling flows in
galaxy clusters is the continued energy input from the cluster central active
galactic nucleus (AGN). However, it remains unclear how this energy is
thermalized, so that it can effectively prevent global thermal instability. One
possible option is that a fraction of the AGN energy is converted to cosmic
rays (CR), which provide non-thermal pressure support, and can retain energy
even as thermal energy is radiated away. By means of magneto-hydrodynamical
simulations, we investigate how CR injected by the AGN jet influence cooling
flows of a massive galaxy cluster. We conclude that converting a fraction of
the AGN luminosity as low as 10\% to CR energy prevents cooling-flows on Gyr
timescales, without significant changes to the structure of the multi-phase
intra-cluster medium. CR-dominated jets, by contrast, lead to the formation of
an extended, warm central nebula that is supported by CR pressure. We report
that the presence of CR is not able to suppress the onset of thermal
instability in massive galaxy clusters, but CR-dominated jets do significantly
change the continued evolution of gas as it continues to cool from isobaric to
isochoric. CR redistribution in the cluster is dominated by advection, rather
than diffusion or streaming, but the heating by CR streaming helps maintain gas
in the hot and warm phase. Observationally, self-regulating, CR-dominated jets
produce a {\gamma}-ray flux in excess of current observational limits, but low
CR fractions in the jet are not ruled out.


title: Existence of $2$-Factors in Tough Graphs without Forbidden Subgraphs

abstract:
  For a given graph $R$, a graph $G$ is $R$-free if $G$ does not contain $R$ as
an induced subgraph. It is known that every $2$-tough graph with at least three
vertices has a $2$-factor. In graphs with restricted structures, it was shown
that every $2K_2$-free $3/2$-tough graph with at least three vertices has a
$2$-factor, and the toughness bound $3/2$ is best possible. In viewing $2K_2$,
the disjoint union of two edges, as a linear forest, in this paper, for any
linear forest $R$ on 5, 6, or 7 vertices, we find the sharp toughness bound $t$
such that every $t$-tough $R$-free graph on at least three vertices has a
2-factor.


title:
Control Barrier Functions with Actuation Constraints under Signal
  Temporal Logic Specifications

abstract:
  We propose control barrier functions (CBFs) for a family of dynamical systems
to satisfy a broad fragment of Signal Temporal Logic (STL) specifications,
which may include subtasks with nested temporal operators or conflicting
requirements (e.g., achieving multiple subtasks within the same time interval).
The proposed CBFs take into account the actuation limits of the dynamical
system as well as a feasible sequence of subtasks, and they define time-varying
feasible sets of states the system must always stay inside. We show some
theoretical results on the correctness of the proposed method. We illustrate
the benefits of the proposed CBFs and compare their performance with the
existing methods via simulations.


title: The Effects of Regularization and Data Augmentation are Class Dependent

abstract:
  Regularization is a fundamental technique to prevent over-fitting and to
improve generalization performances by constraining a model's complexity.
Current Deep Networks heavily rely on regularizers such as Data-Augmentation
(DA) or weight-decay, and employ structural risk minimization, i.e.
cross-validation, to select the optimal regularization hyper-parameters. In
this study, we demonstrate that techniques such as DA or weight decay produce a
model with a reduced complexity that is unfair across classes. The optimal
amount of DA or weight decay found from cross-validation leads to disastrous
model performances on some classes e.g. on Imagenet with a resnet50, the "barn
spider" classification test accuracy falls from $68\%$ to $46\%$ only by
introducing random crop DA during training. Even more surprising, such
performance drop also appears when introducing uninformative regularization
techniques such as weight decay. Those results demonstrate that our search for
ever increasing generalization performance -- averaged over all classes and
samples -- has left us with models and regularizers that silently sacrifice
performances on some classes. This scenario can become dangerous when deploying
a model on downstream tasks e.g. an Imagenet pre-trained resnet50 deployed on
INaturalist sees its performances fall from $70\%$ to $30\%$ on class \#8889
when introducing random crop DA during the Imagenet pre-training phase. Those
results demonstrate that designing novel regularizers without class-dependent
bias remains an open research question.


title:
RESOLVE and ECO: Finding Low-Metallicity $z\sim0$ Dwarf AGN Candidates
  Using Optimized Emission-Line Diagnostics

abstract:
  Existing star-forming/AGN classification schemes using optical emission-line
diagnostics mostly fail for low-metallicity and/or highly star-forming
galaxies, missing AGN in typical $z\sim0$ dwarfs. To recover AGN in dwarfs with
strong emission lines, we present a classification scheme optimizing the use of
existing optical diagnostics. We use SDSS emission-line catalogs overlapping
the volume- and mass-limited RESOLVE and ECO surveys to determine the AGN
percentage in strong emission line dwarfs. Our photoionization grids show that
the [O III]/H$\beta$ versus [S II]/H$\alpha$ diagram (SII plot) and [O
III]/H$\beta$ versus [O I]/H$\alpha$ diagram (OI plot) are less metallicity
sensitive and more successful in identifying dwarf AGN than the popular [O
III]/H$\beta$ versus [N II]/H$\alpha$ diagnostic (NII plot or "BPT diagram").
We identify a new category of "Star Forming-AGN" (SF-AGN) classified as
star-forming by the NII plot but as AGN by the SII and/or OI plots. Including
SF-AGN, we find the $z\sim0$ AGN percentage in dwarfs with strong emission
lines to be $\sim$3-15%, far exceeding most previous optical estimates
($\sim$1%). The large range in our dwarf AGN percentage reflects differences in
spectral fitting methodologies between catalogs. The highly complete nature of
RESOLVE and ECO allows us to normalize strong emission-line galaxy statistics
to the full galaxy population, reducing the dwarf AGN percentage to $\sim$1-2%.
The newly identified SF-AGN are mostly gas-rich dwarfs with halo mass $ <
10^{11.5} M_\odot$, where highly efficient cosmic gas accretion is expected.
Almost all SF-AGN also have low metallicities (Z $\lesssim 0.4$ Z$_\odot$),
demonstrating the advantage of our method.


title: Class-Incremental Learning with Strong Pre-trained Models

abstract:
  Class-incremental learning (CIL) has been widely studied under the setting of
starting from a small number of classes (base classes). Instead, we explore an
understudied real-world setting of CIL that starts with a strong model
pre-trained on a large number of base classes. We hypothesize that a strong
base model can provide a good representation for novel classes and incremental
learning can be done with small adaptations. We propose a 2-stage training
scheme, i) feature augmentation -- cloning part of the backbone and fine-tuning
it on the novel data, and ii) fusion -- combining the base and novel
classifiers into a unified classifier. Experiments show that the proposed
method significantly outperforms state-of-the-art CIL methods on the
large-scale ImageNet dataset (e.g. +10% overall accuracy than the best). We
also propose and analyze understudied practical CIL scenarios, such as
base-novel overlap with distribution shift. Our proposed method is robust and
generalizes to all analyzed CIL settings.


title: Zero-Shot Category-Level Object Pose Estimation

abstract:
  Object pose estimation is an important component of most vision pipelines for
embodied agents, as well as in 3D vision more generally. In this paper we
tackle the problem of estimating the pose of novel object categories in a
zero-shot manner. This extends much of the existing literature by removing the
need for pose-labelled datasets or category-specific CAD models for training or
inference. Specifically, we make the following contributions. First, we
formalise the zero-shot, category-level pose estimation problem and frame it in
a way that is most applicable to real-world embodied agents. Secondly, we
propose a novel method based on semantic correspondences from a self-supervised
vision transformer to solve the pose estimation problem. We further re-purpose
the recent CO3D dataset to present a controlled and realistic test setting.
Finally, we demonstrate that all baselines for our proposed task perform
poorly, and show that our method provides a six-fold improvement in average
rotation accuracy at 30 degrees. Our code is available at
https://github.com/applied-ai-lab/zero-shot-pose.


title:
SurroundDepth: Entangling Surrounding Views for Self-Supervised
  Multi-Camera Depth Estimation

abstract:
  Depth estimation from images serves as the fundamental step of 3D perception
for autonomous driving and is an economical alternative to expensive depth
sensors like LiDAR. The temporal photometric consistency enables
self-supervised depth estimation without labels, further facilitating its
application. However, most existing methods predict the depth solely based on
each monocular image and ignore the correlations among multiple surrounding
cameras, which are typically available for modern self-driving vehicles. In
this paper, we propose a SurroundDepth method to incorporate the information
from multiple surrounding views to predict depth maps across cameras.
Specifically, we employ a joint network to process all the surrounding views
and propose a cross-view transformer to effectively fuse the information from
multiple views. We apply cross-view self-attention to efficiently enable the
global interactions between multi-camera feature maps. Different from
self-supervised monocular depth estimation, we are able to predict real-world
scales given multi-camera extrinsic matrices. To achieve this goal, we adopt
structure-from-motion to extract scale-aware pseudo depths to pretrain the
models. Further, instead of predicting the ego-motion of each individual
camera, we estimate a universal ego-motion of the vehicle and transfer it to
each view to achieve multi-view consistency. In experiments, our method
achieves the state-of-the-art performance on the challenging multi-camera depth
estimation datasets DDAD and nuScenes.


title:
tmVar 3.0: an improved variant concept recognition and normalization
  tool

abstract:
  Previous studies have shown that automated text-mining tools are becoming
increasingly important for successfully unlocking variant information in
scientific literature at large scale. Despite multiple attempts in the past,
existing tools are still of limited recognition scope and precision. We propose
tmVar 3.0: an improved variant recognition and normalization tool. Compared to
its predecessors, tmVar 3.0 is able to recognize a wide spectrum of variant
related entities (e.g., allele and copy number variants), and to group
different variant mentions belonging to the same concept in an article for
improved accuracy. Moreover, tmVar3 provides additional variant normalization
options such as allele-specific identifiers from the ClinGen Allele Registry.
tmVar3 exhibits a state-of-the-art performance with over 90% accuracy in
F-measure in variant recognition and normalization, when evaluated on three
independent benchmarking datasets. tmVar3 is freely available for download. We
have also processed the entire PubMed and PMC with tmVar3 and released its
annotations on our FTP. Availability: ftp://ftp.ncbi.nlm.nih.gov/pub/lu/tmVar3


title:
Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive
  Transformer

abstract:
  Videos are created to express emotion, exchange information, and share
experiences. Video synthesis has intrigued researchers for a long time. Despite
the rapid progress driven by advances in visual synthesis, most existing
studies focus on improving the frames' quality and the transitions between
them, while little progress has been made in generating longer videos. In this
paper, we present a method that builds on 3D-VQGAN and transformers to generate
videos with thousands of frames. Our evaluation shows that our model trained on
16-frame video clips from standard benchmarks such as UCF-101, Sky Time-lapse,
and Taichi-HD datasets can generate diverse, coherent, and high-quality long
videos. We also showcase conditional extensions of our approach for generating
meaningful long videos by incorporating temporal information with text and
audio. Videos and code can be found at
https://songweige.github.io/projects/tats/index.html.


title:
UV Absorption by Silicate Cloud Precursors in Ultra-hot Jupiter
  WASP-178b

abstract:
  Aerosols have been found to be nearly ubiquitous in substellar atmospheres.
The precise temperature at which these aerosols begin to form in exoplanets has
yet to be observationally constrained. Theoretical models and observations of
muted spectral features suggest that silicate clouds play an important role in
exoplanets between at least 950 and 2,100 K. However, some giant planets are
thought to be hot enough to avoid condensation altogether. Here, we present the
near-UV transmission spectrum of an ultra-hot Jupiter, WASP-178b
($\sim$2,450~K), that exhibits significant NUV absorption. This
short-wavelength absorption is among the largest spectral features ever
observed in an exoplanet in terms of atmospheric scale heights. Bayesian
retrievals indicate the presence of gaseous refractory species containing
silicon and magnesium, which are the precursors to condensate clouds at lower
temperatures. SiO in particular has not been detected in exoplanets before, but
the presence of SiO in WASP-178b is consistent with theoretical expectation as
the dominant Si-bearing species at high temperatures. These observations allow
us to re-interpret previous observations of HAT-P-41b and WASP-121b that did
not consider SiO to suggest that silicate cloud formation begins on exoplanets
with equilibrium temperatures between 1,950 and 2,450~K.


title: Equivariance Discovery by Learned Parameter-Sharing

abstract:
  Designing equivariance as an inductive bias into deep-nets has been a
prominent approach to build effective models, e.g., a convolutional neural
network incorporates translation equivariance. However, incorporating these
inductive biases requires knowledge about the equivariance properties of the
data, which may not be available, e.g., when encountering a new domain. To
address this, we study how to discover interpretable equivariances from data.
Specifically, we formulate this discovery process as an optimization problem
over a model's parameter-sharing schemes. We propose to use the partition
distance to empirically quantify the accuracy of the recovered equivariance.
Also, we theoretically analyze the method for Gaussian data and provide a bound
on the mean squared gap between the studied discovery scheme and the oracle
scheme. Empirically, we show that the approach recovers known equivariances,
such as permutations and shifts, on sum of numbers and spatially-invariant
data.


title: Unsupervised Image-to-Image Translation with Generative Prior

abstract:
  Unsupervised image-to-image translation aims to learn the translation between
two visual domains without paired data. Despite the recent progress in image
translation models, it remains challenging to build mappings between complex
domains with drastic visual discrepancies. In this work, we present a novel
framework, Generative Prior-guided UNsupervised Image-to-image Translation
(GP-UNIT), to improve the overall quality and applicability of the translation
algorithm. Our key insight is to leverage the generative prior from pre-trained
class-conditional GANs (e.g., BigGAN) to learn rich content correspondences
across various domains. We propose a novel coarse-to-fine scheme: we first
distill the generative prior to capture a robust coarse-level content
representation that can link objects at an abstract semantic level, based on
which fine-level content features are adaptively learned for more accurate
multi-level content correspondences. Extensive experiments demonstrate the
superiority of our versatile framework over state-of-the-art methods in robust,
high-quality and diversified translations, even for challenging and distant
domains.


title:
Pre-train, Self-train, Distill: A simple recipe for Supersizing 3D
  Reconstruction

abstract:
  Our work learns a unified model for single-view 3D reconstruction of objects
from hundreds of semantic categories. As a scalable alternative to direct 3D
supervision, our work relies on segmented image collections for learning 3D of
generic categories. Unlike prior works that use similar supervision but learn
independent category-specific models from scratch, our approach of learning a
unified model simplifies the training process while also allowing the model to
benefit from the common structure across categories. Using image collections
from standard recognition datasets, we show that our approach allows learning
3D inference for over 150 object categories. We evaluate using two datasets and
qualitatively and quantitatively show that our unified reconstruction approach
improves over prior category-specific reconstruction baselines. Our final 3D
reconstruction model is also capable of zero-shot inference on images from
unseen object categories and we empirically show that increasing the number of
training categories improves the reconstruction quality.


title: Total Variation Optimization Layers for Computer Vision

abstract:
  Optimization within a layer of a deep-net has emerged as a new direction for
deep-net layer design. However, there are two main challenges when applying
these layers to computer vision tasks: (a) which optimization problem within a
layer is useful?; (b) how to ensure that computation within a layer remains
efficient? To study question (a), in this work, we propose total variation (TV)
minimization as a layer for computer vision. Motivated by the success of total
variation in image processing, we hypothesize that TV as a layer provides
useful inductive bias for deep-nets too. We study this hypothesis on five
computer vision tasks: image classification, weakly supervised object
localization, edge-preserving smoothing, edge detection, and image denoising,
improving over existing baselines. To achieve these results we had to address
question (b): we developed a GPU-based projected-Newton method which is
$37\times$ faster than existing solutions.


title: On the Stability of the Repulsive Fermi Gas with Contact Interactions

abstract:
  We report the creation and the study of the stability of a repulsive
quasi-homogeneous spin-$1/2$ Fermi gas with contact interactions. For the range
of scattering lengths $a$ explored, the dominant mechanism of decay is a
universal three-body recombination towards a Feshbach bound state. We observe
that the recombination coefficient $K_3\propto \epsilon_\text{kin} a^6$, where
the first factor, the average kinetic energy per particle
$\epsilon_\text{kin}$, arises from a three-body threshold law, and the second
one from the universality of recombination. Both scaling laws are consequences
of Pauli blocking effects in three-body collisions involving two identical
fermions. As a result of the interplay between Fermi statistics and the
momentum dependence of the recombination process, the system exhibits
non-trivial temperature dynamics during recombination, alternatively heating or
cooling depending on its initial quantum degeneracy. The measurement of $K_3$
provides an upper bound for the interaction strength achievable in equilibrium
for a uniform repulsive Fermi gas.

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


title: DaViT: Dual Attention Vision Transformers

abstract:
  In this work, we introduce Dual Attention Vision Transformers (DaViT), a
simple yet effective vision transformer architecture that is able to capture
global context while maintaining computational efficiency. We propose
approaching the problem from an orthogonal angle: exploiting self-attention
mechanisms with both "spatial tokens" and "channel tokens". With spatial
tokens, the spatial dimension defines the token scope, and the channel
dimension defines the token feature dimension. With channel tokens, we have the
inverse: the channel dimension defines the token scope, and the spatial
dimension defines the token feature dimension. We further group tokens along
the sequence direction for both spatial and channel tokens to maintain the
linear complexity of the entire model. We show that these two self-attentions
complement each other: (i) since each channel token contains an abstract
representation of the entire image, the channel attention naturally captures
global interactions and representations by taking all spatial positions into
account when computing attention scores between channels; (ii) the spatial
attention refines the local representations by performing fine-grained
interactions across spatial locations, which in turn helps the global
information modeling in channel attention. Extensive experiments show our DaViT
achieves state-of-the-art performance on four different tasks with efficient
computations. Without extra data, DaViT-Tiny, DaViT-Small, and DaViT-Base
achieve 82.8%, 84.2%, and 84.6% top-1 accuracy on ImageNet-1K with 28.3M,
49.7M, and 87.9M parameters, respectively. When we further scale up DaViT with
1.5B weakly supervised image and text pairs, DaViT-Gaint reaches 90.4% top-1
accuracy on ImageNet-1K. Code is available at https://github.com/dingmyu/davit.


title:
FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality
  Assessment

abstract:
  Most existing action quality assessment methods rely on the deep features of
an entire video to predict the score, which is less reliable due to the
non-transparent inference process and poor interpretability. We argue that
understanding both high-level semantics and internal temporal structures of
actions in competitive sports videos is the key to making predictions accurate
and interpretable. Towards this goal, we construct a new fine-grained dataset,
called FineDiving, developed on diverse diving events with detailed annotations
on action procedures. We also propose a procedure-aware approach for action
quality assessment, learned by a new Temporal Segmentation Attention module.
Specifically, we propose to parse pairwise query and exemplar action instances
into consecutive steps with diverse semantic and temporal correspondences. The
procedure-aware cross-attention is proposed to learn embeddings between query
and exemplar steps to discover their semantic, spatial, and temporal
correspondences, and further serve for fine-grained contrastive regression to
derive a reliable scoring mechanism. Extensive experiments demonstrate that our
approach achieves substantial improvements over state-of-the-art methods with
better interpretability. The dataset and code are available at
\url{https://github.com/xujinglin/FineDiving}.


title: Adapting CLIP For Phrase Localization Without Further Training

abstract:
  Supervised or weakly supervised methods for phrase localization (textual
grounding) either rely on human annotations or some other supervised models,
e.g., object detectors. Obtaining these annotations is labor-intensive and may
be difficult to scale in practice. We propose to leverage recent advances in
contrastive language-vision models, CLIP, pre-trained on image and caption
pairs collected from the internet. In its original form, CLIP only outputs an
image-level embedding without any spatial resolution. We adapt CLIP to generate
high-resolution spatial feature maps. Importantly, we can extract feature maps
from both ViT and ResNet CLIP model while maintaining the semantic properties
of an image embedding. This provides a natural framework for phrase
localization. Our method for phrase localization requires no human annotations
or additional training. Extensive experiments show that our method outperforms
existing no-training methods in zero-shot phrase localization, and in some
cases, it even outperforms supervised methods. Code is available at
https://github.com/pals-ttic/adapting-CLIP .


title:
SunStage: Portrait Reconstruction and Relighting using the Sun as a
  Light Stage

abstract:
  Outdoor portrait photographs are often marred by the harsh shadows cast under
direct sunlight. To resolve this, one can use post-capture lighting
manipulation techniques, but these methods either require complex hardware
(e.g., a light stage) to capture each individual, or rely on image-based priors
and thus fail to reconstruct many of the subtle facial details that vary from
person to person. In this paper, we present SunStage, a system for accurate,
individually-tailored, and lightweight reconstruction of facial geometry and
reflectance that can be used for general portrait relighting with cast shadows.
Our method only requires the user to capture a selfie video outdoors, rotating
in place, and uses the varying angles between the sun and the face as
constraints in the joint reconstruction of facial geometry, reflectance
properties, and lighting parameters. Aside from relighting, we show that our
reconstruction can be used for applications like reflectance editing and view
synthesis. Results and interactive demos are available at
https://grail.cs.washington.edu/projects/sunstage/.