title: Playing Lottery Tickets in Style Transfer Models

abstract:
  Style transfer has achieved great success and attracted a wide range of
attention from both academic and industrial communities due to its flexible
application scenarios. However, the dependence on a pretty large VGG-based
autoencoder leads to existing style transfer models having high parameter
complexities, which limits their applications on resource-constrained devices.
Compared with many other tasks, the compression of style transfer models has
been less explored. Recently, the lottery ticket hypothesis (LTH) has shown
great potential in finding extremely sparse matching subnetworks which can
achieve on par or even better performance than the original full networks when
trained in isolation. In this work, we for the first time perform an empirical
study to verify whether such trainable matching subnetworks also exist in style
transfer models. Specifically, we take two most popular style transfer models,
i.e., AdaIN and SANet, as the main testbeds, which represent global and local
transformation based style transfer methods respectively. We carry out
extensive experiments and comprehensive analysis, and draw the following
conclusions. (1) Compared with fixing the VGG encoder, style transfer models
can benefit more from training the whole network together. (2) Using iterative
magnitude pruning, we find the matching subnetworks at 89.2% sparsity in AdaIN
and 73.7% sparsity in SANet, which demonstrates that style transfer models can
play lottery tickets too. (3) The feature transformation module should also be
pruned to obtain a much sparser model without affecting the existence and
quality of the matching subnetworks. (4) Besides AdaIN and SANet, other models
such as LST, MANet, AdaAttN and MCCNet can also play lottery tickets, which
shows that LTH can be generalized to various style transfer models.


title:
Opportunistic Qualitative Planning in Stochastic Systems with
  Preferences over Temporal Logic Objectives

abstract:
  Preferences play a key role in determining what goals/constraints to satisfy
when not all constraints can be satisfied simultaneously. In this work, we
study preference-based planning in a stochastic system modeled as a Markov
decision process, subject to a possible incomplete preference over temporally
extended goals. Our contributions are three folds: First, we introduce a
preference language to specify preferences over temporally extended goals.
Second, we define a novel automata-theoretic model to represent the preorder
induced by given preference relation. The automata representation of
preferences enables us to develop a preference-based planning algorithm for
stochastic systems. Finally, we show how to synthesize opportunistic strategies
that achieves an outcome that improves upon the current satisfiable outcome,
with positive probability or with probability one, in a stochastic system. We
illustrate our solution approaches using a robot motion planning example.


title: Distinguishing sets of strong recurrence from van der Corput Sets

abstract:
  We construct a set of strong recurrence which is not a van der Corput set.
This shows that the class of enhanced van der Corput sets is a proper subclass
of sets of strong recurrence. In addition, we derive that the class of sets of
strong recurrence is not a subclass of van der Corput sets. This answers some
questions asked by Bergelson and Lesigne.


title: A continuous proof of the existence of the SLE$_8$ curve

abstract:
  Suppose that $\eta$ is a whole-plane space-filling SLE$_\kappa$ for $\kappa
\in (4,8)$ from $\infty$ to $\infty$ parameterized by Lebesgue measure and
normalized so that $\eta(0) = 0$. For each $T > 0$ and $\kappa \in (4,8)$ we
let $\mu_{\kappa,T}$ denote the law of $\eta|_{[0,T]}$. We show for each $\nu,
T > 0$ that the family of laws $\mu_{\kappa,T}$ for $\kappa \in [4+\nu,8)$ is
compact in the weak topology associated with the space of probability measures
on continuous curves $[0,T] \to {\mathbf C}$ equipped with the uniform
distance. As a direct byproduct of this tightness result (taking a limit as
$\kappa \uparrow 8$), we obtain a new proof of the existence of the SLE$_8$
curve which does not build on the discrete uniform spanning tree scaling limit
of Lawler-Schramm-Werner.


title: Universality for monotone cellular automata

abstract:
  In this paper we study monotone cellular automata in $d$ dimensions. We
develop a general method for bounding the growth of the infected set when the
initial configuration is chosen randomly, and then use this method to prove a
lower bound on the critical probability for percolation that is sharp up to a
constant factor in the exponent for every 'critical' model. This is one of
three papers that together confirm the Universality Conjecture of Bollob\'as,
Duminil-Copin, Morris and Smith.


title:
Differentiability of effective fronts in the continuous setting in two
  dimensions

abstract:
  We study the effective front associated with first-order front propagations
in two dimensions ($n=2$) in the periodic setting with continuous coefficients.
Our main result says that that the boundary of the effective front is
differentiable at every irrational point. Equivalently, the stable norm
associated with a continuous $\mathbb Z^2$-periodic Riemannian metric is
differentiable at irrational points. This conclusion was obtained decades ago
for $C^2$ metrics. There, the $C^2$ regularity was essential to establish
ordering and non-crossing properties of minimal orbits, which no longer exist
in the merely continuous situation. To the best of our knowledge, our result
provides the first nontrivial property of the effective fronts in the
continuous setting, which is the standard assumption in the PDE theory.


title: The critical length for growing a droplet

abstract:
  In many interacting particle systems, relaxation to equilibrium is thought to
occur via the growth of 'droplets', and it is a question of fundamental
importance to determine the critical length at which such droplets appear. In
this paper we construct a mechanism for the growth of droplets in an arbitrary
finite-range monotone cellular automaton on a $d$-dimensional lattice. Our main
application is an upper bound on the critical probability for percolation that
is sharp up to a constant factor in the exponent. Our method also provides
several crucial tools that we expect to have applications to other interacting
particle systems, such as kinetically constrained spin models on
$\mathbb{Z}^d$.
  This is one of three papers that together confirm the Universality Conjecture
of Bollob\'as, Duminil-Copin, Morris and Smith.


title: DOTS: An Open Testbed for Industrial Swarm Robotic Solutions

abstract:
  We present DOTS, a new open access testbed for industrial swarm robotics
experimentation. It consists of 20 fast agile robots with high sensing and
computational performance, and real-world payload capability. They are housed
in an arena equipped with private 5G, motion capture, multiple cameras, and
openly accessible via an online portal. We reduce barriers to entry by
providing a complete platform-agnostic pipeline to develop, simulate, and
deploy experimental applications to the swarm. We showcase the testbed
capabilities with a swarm logistics application, autonomously and reliably
searching for and retrieving multiple cargo carriers.


title:
Fano effect induced giant and robust enhancement of photon correlations
  in cavity QED systems

abstract:
  Fano effect arising from interference between two dissipation channels to the
radiation continuum enables to tune the photon statistics. Understanding the
role of Fano effect and exploiting to achieve strong photon correlations are of
both fundamental and applied significance. We present an analytical description
of Fano-enhanced photon correlations based on cavity quantum electrodynamics to
show that, the Fano effect in atom-cavity systems can improve the degree of
antibunching by over four orders of magnitude. The enhancement factors and the
optimal conditions are explicitly given, and found related to the Fano
parameter $q$. Remarkably, the Fano enhancement manifests robustness against
the decoherence processes, and can survive in the weak coupling regime. We
expect our work to provide insight in tuning the photon statistics through Fano
effect, which offers a new route to enhance the photon correlations, as well as
the possibility of generating nonclassical light in a wider diversity of
systems without the need of strong light-matter interaction.


title: Braided Hopf algebras and gauge transformations

abstract:
  We study infinitesimal gauge transformations of an equivariant noncommutative
principal bundle as a braided Lie algebra of derivations. For this, we analyse
general $K$-braided Hopf and Lie algebras, for $K$ a (quasi)triangular Hopf
algebra of symmetries, and study their representations as braided derivations.
We then study Drinfeld twist deformations of braided Hopf algebras and of Lie
algebras of infinitesimal gauge transformations. We give examples coming from
deformations of abelian and Jordanian type. In particular we explicitly
describe the braided Lie algebra of gauge transformations of the instanton
bundle and of the orthogonal bundle on the quantum sphere $S^4_\theta$.


title: Spatially Multi-conditional Image Generation

abstract:
  In most scenarios, conditional image generation can be thought of as an
inversion of the image understanding process. Since generic image understanding
involves the solving of multiple tasks, it is natural to aim at the generation
of images via multi-conditioning. However, multi-conditional image generation
is a very challenging problem due to the heterogeneity and the sparsity of the
(in practice) available conditioning labels. In this work, we propose a novel
neural architecture to address the problem of heterogeneity and sparsity of the
spatially multi-conditional labels. Our choice of spatial conditioning, such as
by semantics and depth, is driven by the promise it holds for better control of
the image generation process. The proposed method uses a transformer-like
architecture operating pixel-wise, which receives the available labels as input
tokens to merge them in a learned homogeneous space of labels. The merged
labels are then used for image generation via conditional generative
adversarial training. In this process, the sparsity of the labels is handled by
simply dropping the input tokens corresponding to the missing labels at the
desired locations, thanks to the proposed pixel-wise operating architecture.
Our experiments on three benchmark datasets demonstrate the clear superiority
of our method over the state-of-the-art and the compared baselines.


title: A Fock state lattice approach to quantum optics

abstract:
  We analyze a set of models frequently appearing in quantum optical settings
by expressing their Hamiltonians in terms of Fock-state lattices. The few
degrees-of-freedom of such models, together with the system symmetries, make
the emerging Fock-state lattices rather simple such that they can be linked to
known lattice models from the condensed matter community. This sheds new light
on known quantum optical systems. While we provide a rather long list of models
and their corresponding Fock-state lattices, we pick a few ones in order to
demonstrate the strength of the method. The three-mode boson model, for
example, is shown to display a fractal spectrum, and chiral evolution in the
Fock-state lattice characterized by localized distributions traversing along
symmetric trajectories. In a second example we consider the central spin model
which generates a Fock-state lattice reminiscent of the SSH-model hosting
topological edge states. We further demonstrate how the phenomena of flat bands
in lattice models can manifest in related Fock-state lattices, which can be
linked to so called dark states.


title:
Femtoscopic study on $DD^*$ and $D\bar{D}^*$ interactions for $T_{cc}$
  and $X(3872)$

abstract:
  We investigate $DD^*$ and $D\bar{D}^*$ momentum correlations in high-energy
collisions to elucidate the nature of $T_{cc}$ and $X(3872)$ exotic hadrons.
Single range Gaussian potentials with the channel couplings to the isospin
partners are constructed based on the empirical data. The momentum correlation
functions of the $D^0D^{*+}$, $D^+D^{*0}$, $D^0\bar{D}^{*0}$, and $D^+D^{*-}$
pairs are computed with including the coupled-channel effects. We discuss how
the nature of the exotic states are reflected in the behaviors of the
correlation results.


title: Versatile Multi-Modal Pre-Training for Human-Centric Perception

abstract:
  Human-centric perception plays a vital role in vision and graphics. But their
data annotations are prohibitively expensive. Therefore, it is desirable to
have a versatile pre-train model that serves as a foundation for data-efficient
downstream tasks transfer. To this end, we propose the Human-Centric
Multi-Modal Contrastive Learning framework HCMoCo that leverages the
multi-modal nature of human data (e.g. RGB, depth, 2D keypoints) for effective
representation learning. The objective comes with two main challenges: dense
pre-train for multi-modality data, efficient usage of sparse human priors. To
tackle the challenges, we design the novel Dense Intra-sample Contrastive
Learning and Sparse Structure-aware Contrastive Learning targets by
hierarchically learning a modal-invariant latent space featured with continuous
and ordinal feature distribution and structure-aware semantic consistency.
HCMoCo provides pre-train for different modalities by combining heterogeneous
datasets, which allows efficient usage of existing task-specific human data.
Extensive experiments on four downstream tasks of different modalities
demonstrate the effectiveness of HCMoCo, especially under data-efficient
settings (7.16% and 12% improvement on DensePose Estimation and Human Parsing).
Moreover, we demonstrate the versatility of HCMoCo by exploring cross-modality
supervision and missing-modality inference, validating its strong ability in
cross-modal association and reasoning.


title:
Uncovering Temperature-Dependent Exciton-Polariton Relaxation Mechanisms
  in Perovskites

abstract:
  State-of-the-art hybrid perovskites have demonstrated excellent functionality
in photovoltaics and light-emitting applications, and have emerged as a
promising candidate for exciton-polariton (polariton) optoelectronics. In the
strong coupling regime, polariton formation and Bose-Einstein condensation
(BEC) have been demonstrated at room-temperature in several perovskite
formulations. Thermodynamically, low-threshold BEC requires efficient
scattering to $k_{||}$ = 0, and many applications demand precise control of
polariton interactions. Thus far, the primary mechanisms by which polaritons
relax in perovskites remains unclear. In this work, we perform
temperature-dependent measurements of polaritons in low-dimensional hybrid
perovskites with high light-matter coupling strengths ($\hbar \Omega_{Rabi}$ =
260$\pm$5 meV). By embedding the perovskite active layer in a wedged cavity, we
are able to tune the Hopfield coefficients and decouple the primary polariton
relaxation mechanisms in this material for the first time. We observe the
thermal activation of a bottleneck regime, and reveal that this effect can be
overcome by harnessing intrinsic scattering mechanisms arising from the
interplay between the different excitonic species, such as biexciton-assisted
polariton relaxation pathways, and isoenergetic intracavity pumping. We
demonstrate the dependence of the bottleneck suppression on cavity detuning,
and are able to achieve efficient relaxation to $k_{||}$ = 0 even at cryogenic
temperatures. This new understanding contributes to the design of
ultra-low-threshold BEC and condensate control by engineering polariton
dispersions resonant with efficient relaxation pathways, leveraging intrinsic
material scattering mechanisms for next-generation polariton optoelectronics.


title: AutoAvatar: Autoregressive Neural Fields for Dynamic Avatar Modeling

abstract:
  Neural fields such as implicit surfaces have recently enabled avatar modeling
from raw scans without explicit temporal correspondences. In this work, we
exploit autoregressive modeling to further extend this notion to capture
dynamic effects, such as soft-tissue deformations. Although autoregressive
models are naturally capable of handling dynamics, it is non-trivial to apply
them to implicit representations, as explicit state decoding is infeasible due
to prohibitive memory requirements. In this work, for the first time, we enable
autoregressive modeling of implicit avatars. To reduce the memory bottleneck
and efficiently model dynamic implicit surfaces, we introduce the notion of
articulated observer points, which relate implicit states to the explicit
surface of a parametric human body model. We demonstrate that encoding implicit
surfaces as a set of height fields defined on articulated observer points leads
to significantly better generalization compared to a latent representation. The
experiments show that our approach outperforms the state of the art, achieving
plausible dynamic deformations even for unseen motions.
https://zqbai-jeremy.github.io/autoavatar


title:
Toward the End-to-End Optimization of Particle Physics Instruments with
  Differentiable Programming: a White Paper

abstract:
  The full optimization of the design and operation of instruments whose
functioning relies on the interaction of radiation with matter is a super-human
task, given the large dimensionality of the space of possible choices for
geometry, detection technology, materials, data-acquisition, and
information-extraction techniques, and the interdependence of the related
parameters. On the other hand, massive potential gains in performance over
standard, "experience-driven" layouts are in principle within our reach if an
objective function fully aligned with the final goals of the instrument is
maximized by means of a systematic search of the configuration space. The
stochastic nature of the involved quantum processes make the modeling of these
systems an intractable problem from a classical statistics point of view, yet
the construction of a fully differentiable pipeline and the use of deep
learning techniques may allow the simultaneous optimization of all design
parameters.
  In this document we lay down our plans for the design of a modular and
versatile modeling tool for the end-to-end optimization of complex instruments
for particle physics experiments as well as industrial and medical applications
that share the detection of radiation as their basic ingredient. We consider a
selected set of use cases to highlight the specific needs of different
applications.


title: Analysis of persistence-based solar irradiance forecasting benchmarks

abstract:
  In this work we analyse a set of benchmark methods for solar irradiance
forecasting based on the clear-sky index, namely, persistence, climatology,
smart-persistence and convex combination (CC) of persistence and climatology.
To assess the boundaries of the benchmarks, we include in the analysis a simple
least squares regression technique that uses the last five past samples. For
the methods that need data adjustment, we also analyze their variation with
different training sets. This work is done for intra-day forecast of global
solar horizontal irradiance with 10-minutes and hourly forecast horizons. Our
results, obtained in the south-east region of South America known as Pampa
H\'umeda, confirm the benchmark recommendation, showing that the CC method is
exigent for RMSD as it achieves almost the same performance of the more complex
linear combination of past measurements. We also observe that the benchmarks
have different behavior depending on the metric, as the most exigent benchmark
for one metric is not necessarily the most exigent for another.


title: Rough volatility: fact or artefact?

abstract:
  We investigate the statistical evidence for the use of `rough' fractional
processes with Hurst exponent $H< 0.5$ for the modeling of volatility of
financial assets, using a model-free approach. We introduce a non-parametric
method for estimating the roughness of a function based on discrete sample,
using the concept of normalized $p$-th variation along a sequence of
partitions. We investigate the finite sample performance of our estimator for
measuring the roughness of sample paths of stochastic processes using detailed
numerical experiments based on sample paths of fractional Brownian motion and
other fractional processes. We then apply this method to estimate the roughness
of realized volatility signals based on high-frequency observations. Detailed
numerical experiments based on stochastic volatility models show that, even
when the instantaneous volatility has diffusive dynamics with the same
roughness as Brownian motion, the realized volatility exhibits rough behaviour
corresponding to a Hurst exponent significantly smaller than $0.5$. Comparison
of roughness estimates for realized and instantaneous volatility in fractional
volatility models with different values of Hurst exponent shows that,
irrespective of the roughness of the spot volatility process, realized
volatility always exhibits `rough' behaviour with an apparent Hurst index
$\hat{H}<0.5$. These results suggest that the origin of the roughness observed
in realized volatility time-series lies in the microstructure noise rather than
the volatility process itself.


title:
Reactive Whole-Body Obstacle Avoidance for Collision-Free Human-Robot
  Interaction with Topological Manifold Learning

abstract:
  Safe collaboration between human and robots in a common unstructured
environment becomes increasingly critical with the emergence of Industry 4.0.
However, to accomplish safe, robust, and autonomous collaboration with humans,
modern robotic systems must possess not only effective proximity perception but
also reactive obstacle avoidance. Unfortunately, for most robotic systems,
their shared working environment with human operators may not always be static,
instead often dynamically varying and being constantly cluttered with
unanticipated obstacles or hazards. In this paper, we present a novel
methodology of reactive whole-body obstacle avoidance methodology that
safeguards the human who enters the robot's workspace through achieving
conflict-free human-robot interactions even in a dynamically constrained
environment. Unlike existing Jacobian-type or geometric approaches, our
proposed methodology leverages both topological manifold learning and latest
deep learning advances, therefore can not only be readily generalized into
other unseen problem settings, but also achieve high computing efficiency with
concrete theoretical basis. Furthermore, in sharp contrast to the industrial
cobot setting, our methodology allows a robotic arm to proactively avoid
obstacles of arbitrary 3D shapes without direct contacting. To solidify our
study, we implement and validate our methodology with a robotic platform
consisting of dual 6-DoF robotic arms with optimized proximity sensor
placement, both of which are capable of working collaboratively with different
levels of interference. Specifically, one arm will perform reactive whole-body
obstacle avoidance while achieving its pre-determined objective, with the other
arm emulating the presence of a human collaborator with independent and
potentially adversary movements.


title: p-process chaser detector in $n$-$\gamma$ coincidences

abstract:
  We propose two types of neutron-$\gamma_1$-$\gamma_2$ triple coincidence
detectors (not constructed) to chase gamma transitions to produce p-nuclei
following the neutron emission in the $(\gamma, n)$ reaction. Neutrons are
detected with 24 $^3$He counters embedded in a polyethylene moderator in Type I
detector and with 6 liquid scintillation detectors in Type II detector,
respectively. $\gamma$ rays are detected with two high-purity germanium
detectors and four LaBr$_3$(Ce) detectors. The detector which is referred to as
p-process chaser detector is used to search for mediating states in $^{180}$Ta
through which the isomeric and ground states in $^{180}$Ta are thermalized in
the p-process. A search is made for both resonant states and unresolved states
in high nuclear-level-density domain.


title: Status of the IAU Meteor Data Center

abstract:
  Since 2007, the Meteor Data Center (MDC) has had two components: the Orbital
database (OD) and the Shower database (SD). The orbital part is in charge of
the efficient collection, checking, and dissemination of geocentric parameters
and orbits of individual orbits. Is also acts as a central depository for
meteoroid orbits obtained by different techniques: photographic, television,
video, CCD and radar. The shower database collects the geocentric and orbital
parameters of the meteor showers and meteoroids streams. It is not an archive
of all information related to meteor showers, its primary task is to give
unique names and codes to new meteor showers (streams). The SD acts in
conjunction with the Working Group on Meteor Shower Nomenclature of
International Astronomical Union (IAU) Commission F1, "Meteors, Meteorites, and
Interplanetary Dust". In our paper, we give a concise description of the IAU
MDC database, its origin, structure and, in particular, the current
requirements for the introduction of new orbital and shower data.


title:
How in situ atmospheric transmission can affect cosmological constraints
  from type Ia supernovae ?

abstract:
  The measurement of type Ia supernova colours in photometric surveys is the
key to access to cosmological distances. But for future large surveys like the
Large Survey of Space and Time undertaken by the Vera Rubin Observatory in
Chile, the large statistical power of the promised catalogues will make the
photometric calibration uncertainties dominant in the error budget and will
limit our ability to use it for precision cosmology. The knowledge of the
on-site atmospheric transmission on average for the full survey, or for season
or each exposure can help reaching the sub-percent precision for magnitudes. We
will show that measuring the local atmospheric transmission allows to correct
the raw magnitudes to reduce the photometric systematic uncertainties. Then we
will present how this strategy is implemented at the Rubin Observatory via the
Auxiliary Telescope and its slitless spectrograph.


title: X-Ray Redshifts of Obscured Chandra Source Catalog AGN

abstract:
  We have computed obscured AGN redshifts using the XZ method, adopting a broad
treatment in which we employed a wide-ranging data set and worked primarily at
the XZ counts sensitivity threshold, culminating with a redshift catalog
containing 121 sources that lack documented redshifts. We considered 363
obscured AGN from the Chandra Source Catalog Release 2.0, 59 of which were
selected using multiwavelength criteria while 304 were X-ray selected.
One-third of the data set had cross-matched spectroscopic or photometric
redshifts. These sources, dominated by low-$z$ and low-$N_H$ AGN, were
supplemented by 1000 simulations to form a data set for testing the XZ method.
We used a multi-layer perceptron neural network to examine and predict cases in
which XZ fails to reproduce the known redshift, yielding a classifier that can
identify and discard poor redshift estimates. This classifier demonstrated a
statistically significant $\sim$3$\sigma$ improvement over the existing XZ
redshift information gain filter. We applied the machine learning model to
sources with no documented redshifts, resulting in the 121-source new redshift
catalog, all of which were X-ray selected. Our neural network's performance
suggests that nearly 90% of these redshift estimates are consistent with
hypothetical spectroscopic or photometric measurements, strengthening the
notion that redshifts can be reliably estimated using only X-rays, which is
valuable to current and future missions such as Athena. We have also identified
a possible Compton-thick candidate that warrants further investigation.


title: Well-defined equations of motion without constraint of external sources

abstract:
  We present a new approach to constrained classical fields that enables the
action formalism to dictate how external sources must enter the resulting
equations of motion. If symmetries asserted upon the varied fields can be
modeled as restrictions in Fourier space, we prove that these restrictions are
automatically applied to external sources in an unambiguous way. In contrast,
the typical procedure inserts symmetric ansatze into the Euler-Lagrange
differential equations, even for external sources not being solved. This
requires ad hoc constraint of external sources, which can introduce
leading-order errors to model systems despite superficial consistency between
model field and source terms. To demonstrate, we consider Robertson-Walker
cosmologies within General Relativity and prove that the influence of
point-like relativistic pressure sources on cosmological dynamics cannot be
excluded by theoretical arguments.


title: PeV Tau Neutrinos to Unveil Ultra-High-Energy Sources

abstract:
  The observation of ultra-high-energy EeV-energy cosmogenic neutrinos provides
a direct path to identifying the sources of the highest energy cosmic rays;
searches have so far resulted in only upper limits on their flux. However, with
the realization of cubic-kilometer detectors such as IceCube and, in the near
future, KM3NeT, GVD-Baikal, and similar instruments, we anticipate the
observation of PeV-energy cosmic neutrinos with high statistics. In this
context, we draw attention to the opportunity to identify EeV tau neutrinos at
PeV energy using Earth-traversing tau neutrinos. We show that Cherenkov
detectors can improve their sensitivity to transient point sources by more than
an order of magnitude by indirectly observing EeV tau neutrinos with initial
energies that are nominally beyond their reach. This new technique also
improves their sensitivity to the ultra-high-energy diffuse neutrino flux by up
to a factor of two. Our work exemplifies how observing tau neutrinos at PeV
energies provides an unprecedented reach to EeV fluxes.


title: Worldsheet Correlators in Black Hole Microstates

abstract:
  Light probes interacting with heavy bound states such as black holes give
rise to interesting observables containing valuable dynamical information.
Recently, a family of black hole microstates was shown to admit an exact string
worldsheet description. We construct the physical vertex operators of these
models, and derive their form in the low-energy AdS$_3$ limit. We then compute
an extensive set of novel heavy-light correlators in these backgrounds. A
subset of these provide the first match between worldsheet correlators in black
hole microstates and the holographically dual conformal field theory, in a
highly non-trivial way. We further conjecture a closed formula for heavy-light
correlators with an arbitrary number of light insertions. As an application, we
compute the analogue of the Hawking radiation rate for these microstates.


title:
Using Old and New Approaches: Determining Physical Properties of Brown
  Dwarfs with Empirical Relations and Machine Learning Models

abstract:
  We investigate applications of machine learning models to directly infer
physical properties of brown dwarfs from their photometry and spectra using
$\textit{The Cannon}$. We demonstrate that absolute magnitudes, spectral types,
and spectral indices can be determined from low-resolution SpeX prism spectra
of L and T dwarfs without trigonometric parallax measurements and with
precisions competitive with commonly used methods. For T dwarfs with
sufficiently precise spectra and photometry, bolometric luminosities and
effective temperatures can be determined at precisions comparable to methods
that use polynomial relations as a function of absolute magnitudes. We also
provide new and updated polynomial relations for absolute magnitudes as a
function of spectral types L0-T8 in 14 bands spanning Pan-STARRS $r_{P1}$ to
AllWISE $\textit{W3}$, using a volume-limited sample of 256 brown dwarfs
defined entirely by parallaxes. These include the first relations for brown
dwarfs using Pan-STARRS1 photometry and the first for several infrared bands
using a volume-limited sample. We find that our novel method with $\textit{The
Cannon}$ can infer absolute magnitudes with equal or smaller uncertainties than
the polynomial relations that depend on trigonometric parallax measurements.


title:
Strong Cosmic Censorship and Eigenvalue Repulsions for rotating de
  Sitter black holes in higher-dimensions

abstract:
  It has been established that Christodoulou's formulation of Strong Cosmic
Censorship (SCC) is violated by Reissner-Nordstr\"om-de Sitter black holes, but
holds in four-dimensional Kerr-de Sitter black holes. We show that SCC is also
respected by equal angular momenta (cohomogeneity-1) Myers-Perry-de Sitter
(MP-dS) in odd $d \ge 5$ spacetime dimensions. This suggests that the
preservation of SCC in rotating backgrounds might be a universal property of
Einstein gravity and not limited to the d = 4 Kerr-dS background. As required
to discuss SCC in de Sitter spacetimes, we also study important aspects of the
scalar field quasinormal mode (QNM) spectra of MP-dS. In particular, we find
eigenvalue repulsions similar to those recently observed in the QNM spectra of
asymptotically flat Kerr-Newman black holes. For axisymmetric modes (i.e. with
azimuthal quantum number m = 0) there are three distinct families of QNM (de
Sitter, photon sphere and near-horizon). However, typically, for
non-axisymmetric ($m \ne 0$) QNMs, we find that the entire spectra can be
described by just two families of QNM (since several overtone sections of the
photon sphere and near-horizon families merge). For completeness, we also study
the full scalar field QNM spectra of higher-dimensional Schwarzschild-de Sitter
black holes.


title:
Bioclimatic change as a function of global warming from CMIP6 climate
  projections

abstract:
  Climate change is predicted to lead to major changes in terrestrial
ecosystems. However, significant differences in climate model projections for
given scenarios of greenhouse gas emissions, continue to hinder detailed
assessment. Here we show, using a traditional Koppen-Geiger bioclimate
classification system, that the latest CMIP6 Earth System Models actually agree
very well on the fraction of the global land-surface that will undergo a
significant change per degree of global warming. Data from historical and
ssp585 model runs are used to create bioclimate maps at various degrees of
global warming, and to investigate the performance of the ensemble mean when
classifying climate data into discrete categories. Using a streamlined scheme
with 13 classifications, global bioclimate classification maps at 2K and 4K of
global warming above a 1901-1931 reference period are presented. These
projections show large shifts in bioclimate distribution, with an almost
exclusive change from colder, wetter bioclimates to hotter, dryer ones.
Historical model run performance is assessed and examined by comparison with
the bioclimatic classifications derived from the observed climate over the same
time period. The fraction of the land experiencing a change in its bioclimatic
class as a function of global warming is estimated by combining the results
from the individual models. Despite the discrete nature of the bioclimatic
classification scheme, we find only a weakly-saturating dependence of this
fraction on global warming which implies about 12 pct of land experiencing a
significant change in climate, per 1K increase in global mean temperature
between the global warming levels of 1 and 3K. Therefore, we estimate that
stabilising the climate at 1.5K rather than 2K of global warming, would save
over 7 million square kilometres of land from a major bioclimatic change.


title:
High-order post-Newtonian expansion of the redshift invariant for
  eccentric-orbit non-spinning extreme-mass-ratio inspirals

abstract:
  We calculate the eccentricity dependence of the high-order post-Newtonian
(PN) series for the generalized redshift invariant $\langle u^t \rangle_\tau$
for eccentric-orbit extreme-mass-ratio inspirals on a Schwarzschild background.
These results are calculated within first-order black hole perturbation theory
(BHPT) using Regge-Wheeler-Zerilli (RWZ) gauge. Our \textsc{Mathematica} code
is based on a familiar procedure, using PN expansion of the
Mano-Suzuki-Takasugi (MST) analytic function formalism for $l$ modes up to a
certain maximum and then using a direct general-$l$ PN expansion of the RWZ
equation for arbitrarily high $l$. We calculate dual expansions in PN order and
in powers of eccentricity, reaching 10PN relative order and $e^{20}$. Detailed
knowledge of the eccentricity expansion at each PN order allows us to find
within the eccentricity dependence numerous closed-form expressions and
multiple infinite series with known coefficients. We find leading logarithm
sequences in the PN expansion of the redshift invariant that reflect a similar
behavior in the PN expansion of the energy flux to infinity. A set of flux
terms and special functions that appear in the energy flux, like the
Peters-Mathews flux itself, are shown to reappear in the redshift PN expansion.


title:
When removing an independent set is optimal for reducing the chromatic
  number

abstract:
  How large must the chromatic number of a graph be, in terms of the graph's
maximum degree, to ensure that the most efficient way to reduce the chromatic
number by removing vertices is to remove an independent set? By a reduction to
a powerful, known stability form of Brooks' theorem, we answer this question
precisely, determining the threshold to within two values (and indeed sometimes
a unique value) for graphs of sufficiently large maximum degree.


title:
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved
  Neural Network Calibration

abstract:
  Deep Neural Networks ( DNN s) are known to make overconfident mistakes, which
makes their use problematic in safety-critical applications. State-of-the-art (
SOTA ) calibration techniques improve on the confidence of predicted labels
alone and leave the confidence of non-max classes (e.g. top-2, top-5)
uncalibrated. Such calibration is not suitable for label refinement using
post-processing. Further, most SOTA techniques learn a few hyper-parameters
post-hoc, leaving out the scope for image, or pixel specific calibration. This
makes them unsuitable for calibration under domain shift, or for dense
prediction tasks like semantic segmentation. In this paper, we argue for
intervening at the train time itself, so as to directly produce calibrated DNN
models. We propose a novel auxiliary loss function: Multi-class Difference in
Confidence and Accuracy ( MDCA ), to achieve the same MDCA can be used in
conjunction with other application/task-specific loss functions. We show that
training with MDCA leads to better-calibrated models in terms of Expected
Calibration Error ( ECE ), and Static Calibration Error ( SCE ) on image
classification, and segmentation tasks. We report ECE ( SCE ) score of 0.72
(1.60) on the CIFAR 100 dataset, in comparison to 1.90 (1.71) by the SOTA.
Under domain shift, a ResNet-18 model trained on PACS dataset using MDCA gives
an average ECE ( SCE ) score of 19.7 (9.7) across all domains, compared to 24.2
(11.8) by the SOTA. For the segmentation task, we report a 2X reduction in
calibration error on PASCAL - VOC dataset in comparison to Focal Loss. Finally,
MDCA training improves calibration even on imbalanced data, and for natural
language classification tasks. We have released the code here: code is
available at https://github.com/mdca-loss


title: New class of solutions in the non-minimal O(3)-sigma model

abstract:
  For the study of topological vortices with non-minimal coupling, we built a
kind of non-canonical O(3)-sigma model, with a Maxwell term modified by a
dielectric function. Through the BPS formalism, an investigation is made on
possible configurations of vortices in topological sectors of the sigma model
and the real scalar field. For a particular ansatz, the solutions of the
topological sector of the real scalar field are described by the known kink
solutions. On the other hand, when studying the vortices in the non-minimal
sector of the pure O(3)-sigma model, it is detected the emergence of solutions
that generate solitary waves similar to structures derived from a KdV-like
theory. We observed that in the study of mixed models, namely, the topological
sector of the O(3)-sigma model coupled to the topological sector of the real
scalar field, the vortex solutions assume a profile of a step function. Then,
when kinks of the topological sector of the scalar field are interacting with
the field of the sigma model, it makes the field solutions of the O(3)-sigma
model become extremely localized, making the vortice structures non-physical.


title: Let's reinvent subtraction

abstract:
  Subtraction is a powerful technique for creating new bijections from old.
Let's reinvent it! While we're at it, let's reinvent division as well.


title: Simultaneous Identification and Denoising of Dynamical Systems

abstract:
  In recent years there has been a push to discover the governing equations
dynamical systems directly from measurements of the state, often motivated by
systems that are too complex to directly model. Although there has been
substantial work put into such a discovery, doing so in the case of large noise
has proved challenging. Here we develop an algorithm for Simultaneous
Identification and Denoising of a Dynamical System (SIDDS). We infer the noise
in the state measurements by requiring that the denoised data satisfies the
dynamical system with an equality constraint. This is unlike existing work
where the mismatch in the dynamical system is treated as a penalty in the
objective. We assume the dynamics is represented in a pre-defined basis and
develop a sequential quadratic programming approach to solve the SIDDS problem
featuring a direct solution of KKT system with a specialized preconditioner. In
addition, we show how we can include sparsity promoting regularization using an
iteratively reweighted least squares approach. The resulting algorithm leads to
estimates of the dynamical system that approximately achieve the Cram\'er-Rao
lower bound and, with sparsity promotion, can correctly identify the sparsity
structure for higher levels of noise than existing techniques. Moreover,
because SIDDS decouples the data from the evolution of the dynamical system, we
show how to modify the problem to accurately identify systems from low sample
rate measurements. The inverse problem approach and solution framework used by
SIDDS has the potential to be expanded to related problems identifying
governing equations from noisy data.


title:
Analyzing Generalization of Vision and Language Navigation to Unseen
  Outdoor Areas

abstract:
  Vision and language navigation (VLN) is a challenging visually-grounded
language understanding task. Given a natural language navigation instruction, a
visual agent interacts with a graph-based environment equipped with panorama
images and tries to follow the described route. Most prior work has been
conducted in indoor scenarios where best results were obtained for navigation
on routes that are similar to the training routes, with sharp drops in
performance when testing on unseen environments. We focus on VLN in outdoor
scenarios and find that in contrast to indoor VLN, most of the gain in outdoor
VLN on unseen data is due to features like junction type embedding or heading
delta that are specific to the respective environment graph, while image
information plays a very minor role in generalizing VLN to unseen outdoor
areas. These findings show a bias to specifics of graph representations of
urban environments, demanding that VLN tasks grow in scale and diversity of
geographical environments.


title: A note on co-dimension 2 defects in N=4,d=7 gauged supergravity

abstract:
  In this note we present a solution of $N=4,d=7$ gauged supergravity which is
holographically dual to a co-dimension two defect living in a six dimensional
SCFT. The solution is obtained by double analytic continuation of a two charge
supersymmetric black hole solution. The condition that no conical deficits are
present in the bulk and on the boundary is satisfied by a one parameter family
of solutions for which some holographic observables are computed.


title:
Hybrid Parallelization of Euler-Lagrange Simulations Based on MPI-3
  Shared Memory

abstract:
  The use of Euler-Lagrange methods on unstructured grids extends their
application area to more versatile setups. However, the lack of a regular
topology limits the scalability of distributed parallel methods, especially for
routines that perform a physical search in space. One of the most prominent
slowdowns is the search for halo elements in physical space for the purpose of
runtime communication avoidance. In this work, we present a new
communication-free halo element search algorithm utilizing the MPI-3 shared
memory model. This novel method eliminates the severe performance bottleneck of
many-to-many communication during initialization compared to the distributed
parallelization approach and extends the possible applications beyond those
achievable with the previous approach. Building on these data structures, we
then present methods for efficient particle emission, scalable deposition
schemes for particle-field coupling, and latency hiding approaches. The scaling
performance of the proposed algorithms is validated through plasma dynamics
simulations of an open-source framework on a massively parallel system,
demonstrating an efficiency of up to 80% on 131000 cores.


title:
Impact of dipole-dipole interactions on motility-induced phase
  separation

abstract:
  We present a hydrodynamic theory for systems of dipolar active Brownian
particles which, in the regime of weak dipolar coupling, predicts the onset of
motility-induced phase separation (MIPS), consistent with Brownian dynamics
(BD) simulations. The hydrodynamic equations are derived by explicitly
coarse-graining the microscopic Langevin dynamics, thus allowing for a
quantitative comparison of parameters entering the coarse-grained model and
particle-resolved simulations. Performing BD simulations at fixed density, we
find that dipolar interactions tend to hinder MIPS, as first reported in [Liao
et al., Soft Matter, 2020, 16, 2208]. Here we demonstrate that the theoretical
approach indeed captures the suppression of MIPS. Moreover, the analysis of the
numerically obtained, angle-dependent correlation functions sheds light into
the underlying microscopic mechanisms leading to the destabilization of the
homogeneous phase.


title: Precision Early Universe Cosmology from Stochastic Gravitational Waves

abstract:
  The causal tail of stochastic gravitational waves can be used to probe the
energy density in free streaming relativistic species as well as measure
$g_\star(T)$ and beta functions $\beta(T)$ as a function of temperature. In the
event of the discovery of loud stochastic gravitational waves, we demonstrate
that LISA can measure the free streaming fraction of the universe down to the
the $10^{-3}$ level, 100 times more sensitive than current constraints.
Additionally, it would be sensitive to $\mathcal{O}(1)$ deviations of $g_\star$
and the QCD $\beta$ function from their Standard Model value at temperatures
$\sim 10^5$ GeV. In this case, many motivated models such as split SUSY and
other solutions to the Electroweak Hierarchy problem would be tested. Future
detectors, such as DECIGO, would be 100 times more sensitive than LISA to these
effects and be capable of testing other motivated scenarios such as WIMPs and
axions. The amazing prospect of using precision gravitational wave measurements
to test such well motivated theories provides a benchmark to aim for when
developing a precise understanding of the gravitational wave spectrum both
experimentally and theoretically.


title: Quantifying Demonstration Quality for Robot Learning and Generalization

abstract:
  Learning from Demonstration (LfD) seeks to democratize robotics by enabling
diverse end-users to teach robots to perform a task by providing
demonstrations. However, most LfD techniques assume users provide optimal
demonstrations. This is not always the case in real applications where users
are likely to provide demonstrations of varying quality, that may change with
expertise and other factors. Demonstration quality plays a crucial role in
robot learning and generalization. Hence, it is important to quantify the
quality of the provided demonstrations before using them for robot learning. In
this paper, we propose quantifying the quality of the demonstrations based on
how well they perform in the learned task. We hypothesize that task performance
can give an indication of the generalization performance on similar tasks. The
proposed approach is validated in a user study (N = 27). Users with different
robotics expertise levels were recruited to teach a PR2 robot a generic task
(pressing a button) under different task constraints. They taught the robot in
two sessions on two different days to capture their teaching behaviour across
sessions. The task performance was utilized to classify the provided
demonstrations into high-quality and low-quality sets. The results show a
significant Pearson correlation coefficient (R = 0.85, p < 0.0001) between the
task performance and generalization performance across all participants. We
also found that users clustered into two groups: Users who provided
high-quality demonstrations from the first session, assigned to the
fast-adapters group, and users who provided low-quality demonstrations in the
first session and then improved with practice, assigned to the slow-adapters
group. These results highlight the importance of quantifying demonstration
quality, which can be indicative of the adaptation level of the user to the
task.


title:
Emergent intersubband-plasmon-polaritons of Dirac electrons under
  one-dimensional superlattices

abstract:
  Artifical superlattice (SL) potentials have been employed extensively for
band structure engineering of two-dimensional (2D) Dirac electron gas in
graphene. While such engineered electronic band structures can modify optical
or plasmonic properties of graphene, an emergent polaritonic behavior beyond
weak perturbative effects (e.g. anisotropic Drude weights) has not been
reported. Here, we show that an extreme modulation of one-dimensional (1D) SL
potentials in monolayer graphene deforms the underlying Dirac band dispersion
and introduces ladder-like energy levels near the Fermi surface, which result
in emergent intersubband polaritonic responses in optical conductivity. In our
proposed system, hBN-encapsulated graphene is placed on top of a 1D periodic
metagate. In addition, a backgate placed beneath the metagate is used as the
second gate, further modulating carrier density on regions in graphene that are
not directly screened by the metagate. With a strong carrier density
modulation, graphene electrons experience an array of deep potential wells, and
at large enough momenta perpendicular to the modulation direction, Dirac
electrons are waveguided via total internal reflections. These waveguided modes
appear as flat subbands with nearly equispaced energy levels. As a result,
there arise hybrid intersubband-polaritons with ultra-strong coupling in
plasmonic dispersions. Our study opens up an avenue for exploring emergent
polaritons in two-dimensional materials with gate-tunable electronic band
structures.


title:
Local Continuity of Angular Momentum and Noether Charge for Matter in
  General Relativity

abstract:
  Conservation laws have many applications in numerical relativity. However, it
is not straightforward to define local conservation laws for general dynamic
spacetimes due the lack of coordinate translation symmetries. In flat space,
the rate of change of energy-momentum within a finite spacelike volume is
equivalent to the flux integrated over the surface of this volume; for general
spacetimes it is necessary to include a volume integral of a source term
arising from spacetime curvature. In this work a study of continuity of matter
in general relativity is extended to include angular momentum of matter and
Noether currents associated with gauge symmetries. Expressions for the Noether
charge and flux of complex scalar fields and complex Proca fields are found
using this formalism. Expressions for the angular momentum density, flux and
source are also derived which are then applied to a numerical relativity
collision of boson stars in 3D with non-zero impact parameter as an
illustration of the methods.


title: Multi-flavour SMBH seeding and evolution in cosmological environments

abstract:
  We study the genesis, environment and growth of supermassive black hole
(SMBH) seeds from different formation channels, from PopIII remnants to massive
seeds, modeled within the L-Galaxies semi-analytic code. We run the model on
the merger trees of the Millennium-II simulation (MR-II), as its high halo-mass
resolution (Mvir~10^7 Msun/h) allows to study in a relatively large
cosmological volume (Lbox=100 Mpc/h) the evolution of atomic-cooling halos
where intermediate-mass and heavy seeds are expected to form. We track the
formation of these seeds according to the background and the local variations
of the chemical enrichment and the UV-flux produced by star formation. Not
being able to resolve the first mini-halos, we account for the formation of
PopIII remnants in a sub-grid fashion, using the results of the GQd model. We
find that the descendants of light seeds numerically dominate among the
population of SMBHs at all masses and z. The much rarer intermediate-mass and
heavy seeds form in dense environments where close neighbors provide the
required UV illumination. We also include the formation of heavy seeds in the
mergers of gas-rich massive galaxies, who are very rare in the MR-II volume. We
then track the evolution through gas accretion and mergers of this
multi-flavour population of BH seeds down to z=0. Our model produces a
population of SMBHs whose statistical properties at z=0 meet current
constraints. In the dwarf-mass regime, we predict that the scaling relation
between BH and stellar mass is flatter than in the high-mass range. We also
find that the BH occupation fraction highly depends on the seeding efficiency.
Finally, a fraction of BHs hosted in dwarf galaxies at z=0 never grow since
their formation at z>6. This supports the idea that some local low-mass systems
might harbor central compact objects which are remnant of high-z BH-formation
processes.


title: Cluster Algebras: Network Science and Machine Learning

abstract:
  Cluster algebras have recently become an important player in mathematics and
physics. In this work, we investigate them through the lens of modern data
science, specifically with techniques from network science and
machine-learning. Network analysis methods are applied to the exchange graphs
for cluster algebras of varying mutation types. The analysis indicates that
when the graphs are represented without identifying by permutation equivalence
between clusters an elegant symmetry emerges in the quiver exchange graph
embedding. The ratio between number of seeds and number of quivers associated
to this symmetry is computed for finite Dynkin type algebras up to rank 5, and
conjectured for higher ranks. Simple machine learning techniques successfully
learn to differentiate cluster algebras from their seeds. The learning
performance exceeds 0.9 accuracies between algebras of the same mutation type
and between types, as well as relative to artificially generated data.


title: Optimal quantum kernels for small data classification

abstract:
  While quantum machine learning (ML) has been proposed to be one of the most
promising applications of quantum computing, how to build quantum ML models
that outperform classical ML remains a major open question. Here, we
demonstrate an algorithm for constructing quantum kernels for support vector
machines that adapts quantum gate sequences to data. The algorithm includes
three essential ingredients: greedy search in the space of quantum circuits,
Bayesian information criterion as circuit selection metric and Bayesian
optimization of the parameters of the optimal quantum circuit identified. The
performance of the resulting quantum models for classification problems with a
small number of training points significantly exceeds that of optimized
classical models with conventional kernels. In addition, we illustrate the
possibility of mapping quantum circuits onto molecular fingerprints and show
that performant quantum kernels can be isolated in the resulting chemical
space. This suggests that methods developed for optimization and interpolation
of molecular properties across chemical spaces can be used for building quantum
circuits for quantum machine learning with enhanced performance.


title: The Survival of Ernst Ising and the Struggle to Solve His Model

abstract:
  The life of Ernst Ising and the steps to solving the model named after him
are reported in parallel. Wilhelm Lenz suggested his student Ernst Ising to
explain the existence of ferromagnetism on the basis of his publication in
1920. The result, published in 1925 was disappointing, because only the one
dimensional case could be solved with a negative result about the absence of
ferromagnetism. Wolfgang Pauli who was an assistant of Lenz in Hamburg
published in the same year his 'nonclassical ambiguity', later identified as
the spin of the electron, and the exclusion principle. He was the first - at
the Solvay Conference in 1930 - to present the Hamiltonian of the Ising model
as we know it today.
  Meanwhile Ising had left university research and due to the political
situation in 1938 had to leave Germany and fled to Luxemburg. This went in hand
with damaging the network of researchers dealing with the problem of
ferromagnetism and more generally with phase transitions and statistical
physics. In 1944, the year when Luxemburg was liberated by the American troops
and Ising and his family was rescued, Lars Onsager presented a solution of the
two-dimensional case. In 1952 Chen-Ning Yang solved the problem of Ising's
thesis in two dimensions; one year later Ising became the US citizenship. The
following development showed, that the model turned out to be a highway to
modern physics concepts applicable also in other fields, although the final
exact solution in three dimensions has not yet been reached.


title: Inverse Regge poles problem on a warped ball

abstract:
  In this paper, we study a new type of inverse problem on warped product
Riemannian manifolds with connected boundary that we name warped balls. Using
the symmetry of the geometry, we first define the set of Regge poles as the
poles of the meromorphic continuation of the Dirichlet-to-Neumann map with
respect to the complex angular momentum appearing in the separation of
variables procedure. These Regge poles can also be viewed as the set of
eigenvalues and resonances of a one-dimensional Schr\"odinger equation on the
half-line, obtained after separation of variables. Secondly, we find a precise
asymptotic localisation of the Regge poles in the complex plane and prove that
they uniquely determine the warping function of the warped balls.


title: Future Directions for Active Matter on Ordered Substrates

abstract:
  Active matter is a term encompassing particle-based assemblies with some form
of self-propulsion, including certain biological systems as well as synthetic
systems such as artificial colloidal swimmers, all of which can exhibit a
remarkable variety of new kinds of nonequilibrium phenomena. A wealth of
non-active condensed matter systems can be described in terms of a collection
of particles coupled to periodic substrates, leading to the emergence of
commensurate-incommensurate effects, Mott phases, tribology effects, and
pattern formation. It is natural to ask how such phases are modified when the
system is active. Here we provide an overview and future directions for
studying individual and collectively interacting active matter particles
coupled to periodic substrates, where new types of commensuration effects,
directional locking, and active phases can occur. Further directions for
exploration include locking effects, the realization of active solitons or
active defects in incommensurate phases, active Mott phases, active artificial
spin ice, active doping transitions, active floating phases, active surface
physics, active matter time crystals, and active tribology.


title:
Review on stochastic approach to inflation and primordial black hole
  formation

abstract:
  We present a review on the state-of-the-art of the mathematical framework
known as stochastic inflation, paying special attention to its derivation and
giving references for the readers interested on results coming from the
application of the stochastic framework to different inflationary scenarios,
especially to those of interest for primordial black hole formation. During the
derivation of the stochastic formalism, we will emphasise two aspects in
particular: the difference between the separate universe approach and the true
long wavelength limit of scalar inhomogeneities and the generically
non-Markovian nature of the noises that appear in the stochastic equations.


title:
Collision of protostellar jets in the star-forming region IC 1396N.
  Analysis of knot proper motions

abstract:
  The bright-rimmed cloud IC 1396N is believed to host one of the few known
cases where two bipolar CO outflows driven by young stellar objects actually
collide. The CO outflows are traced by chains of knots of H_2 emission, with
enhanced emission at the position of the possible collision.
  The aim of this work is to use the proper motions of the H_2 knots to confirm
the collision scenario.
  A second epoch H_2 image was obtained, and the proper motions of the knots
were determined with a time baseline of ~11 years. We also performed
differential photometry on the images to check the flux variability of the
knots.
  For each outflow (N and S) we classified the knots as pre-collision or
post-collision. The axes of the pre-collision knots, the position of the
possible collision point, and the axes of the post-collision knots were
estimated. The difference between the proper motion direction of the
post-collision knots and the position angle from the collision point was also
calculated. For some of the knots we obtained the 3D velocity by using the
radial velocity derived from H_2 spectra.
  The velocity pattern of the H_2 knots in the area of interaction
(post-collision knots) shows a deviation from that of the pre-collision knots,
consistent with being a consequence of the interaction between the two
outflows. This favours the interpretation of the IC 1396N outflows as a true
collision between two protostellar jets instead of a projection effect.


title: Quasi-Newton Iteration in Deterministic Policy Gradient

abstract:
  This paper presents a model-free approximation for the Hessian of the
performance of deterministic policies to use in the context of Reinforcement
Learning based on Quasi-Newton steps in the policy parameters. We show that the
approximate Hessian converges to the exact Hessian at the optimal policy, and
allows for a superlinear convergence in the learning, provided that the policy
parametrization is rich. The natural policy gradient method can be interpreted
as a particular case of the proposed method. We analytically verify the
formulation in a simple linear case and compare the convergence of the proposed
method with the natural policy gradient in a nonlinear example.


title: A remark on deformations of $1$-convex manifolds with exceptional curves

abstract:
  A formula for the dimension of the smoothing component of a $3$-dimensional
isolated Cohen--Macaulay singularity is shown. We apply this formula for a
$1$-convex threefold with a connected exceptional curve which is blown down to
a terminal Gorenstein singularity.


title:
Which Generative Adversarial Network Yields High-Quality Synthetic
  Medical Images: Investigation Using AMD Image Datasets

abstract:
  Deep learning has been proposed for the assessment and classification of
medical images. However, many medical image databases with appropriately
labeled and annotated images are small and imbalanced, and thus unsuitable to
train and validate such models. The option is to generate synthetic images and
one successful technique has been patented which limits its use for others. We
have developed a free-access, alternate method for generating synthetic
high-resolution images using Generative Adversarial Networks (GAN) for data
augmentation and showed their effectiveness using eye-fundus images for
Age-Related Macular Degeneration (AMD) identification. Ten different GAN
architectures were compared to generate synthetic eye-fundus images with and
without AMD. Data from three public databases were evaluated using the
Fr\'echet Inception Distance (FID), two clinical experts and deep-learning
classification. The results show that StyleGAN2 reached the lowest FID
(166.17), and clinicians could not accurately differentiate between real and
synthetic images. ResNet-18 architecture obtained the best performance with 85%
accuracy and outperformed the two experts in detecting AMD fundus images, whose
average accuracy was 77.5%. These results are similar to a recently patented
method, and will provide an alternative to generating high-quality synthetic
medical images. Free access has been provided to the entire method to
facilitate the further development of this field.


title: Why and how to add direction to a quantum walk

abstract:
  We formalize the treatment of directed (or chiral) quantum walks using
Hermitian adjacency matrices, bridging two developing fields of research in
quantum information and spectral graph theory. We display results and
simulations which highlight the conceptual differences between having
directions encoded in the Hamiltonians or not. This leads to a construction of
a new type of quantum phenomenon: zero transfer between pairs of sites in a
connected coupled network, which is only possible in the directed model we
study. Our main result is a description of several families of directed cycles
that admit zero transfer.


title: $\omega$-Forest Algebras and Temporal Logics

abstract:
  We use the algebraic framework for languages of infinite trees introduced in
[4] to derive effective characterisations of various temporal logics, in
particular the logic EF (a fragment of CTL) and its counting variant cEF.


title:
TimeReplayer: Unlocking the Potential of Event Cameras for Video
  Interpolation

abstract:
  Recording fast motion in a high FPS (frame-per-second) requires expensive
high-speed cameras. As an alternative, interpolating low-FPS videos from
commodity cameras has attracted significant attention. If only low-FPS videos
are available, motion assumptions (linear or quadratic) are necessary to infer
intermediate frames, which fail to model complex motions. Event camera, a new
camera with pixels producing events of brightness change at the temporal
resolution of $\mu s$ $(10^{-6}$ second $)$, is a game-changing device to
enable video interpolation at the presence of arbitrarily complex motion. Since
event camera is a novel sensor, its potential has not been fulfilled due to the
lack of processing algorithms. The pioneering work Time Lens introduced event
cameras to video interpolation by designing optical devices to collect a large
amount of paired training data of high-speed frames and events, which is too
costly to scale. To fully unlock the potential of event cameras, this paper
proposes a novel TimeReplayer algorithm to interpolate videos captured by
commodity cameras with events. It is trained in an unsupervised
cycle-consistent style, canceling the necessity of high-speed training data and
bringing the additional ability of video extrapolation. Its state-of-the-art
results and demo videos in supplementary reveal the promising future of
event-based vision.


title:
Experimental observation of the geostrophic turbulence regime of rapidly
  rotating convection

abstract:
  The competition between turbulent convection and global rotation in planetary
and stellar interiors governs the transport of heat and tracers, as well as
magnetic-field generation. These objects operate in dynamical regimes ranging
from weakly rotating convection to the `geostrophic turbulence' regime of
rapidly rotating convection. However, the latter regime has remained elusive in
the laboratory, despite a worldwide effort to design ever-taller rotating
convection cells over the last decade. Building on a recent experimental
approach where convection is driven radiatively, we report heat transport
measurements in quantitative agreement with this scaling regime, the
experimental scaling-law being validated against direct numerical simulations
(DNS) of the idealized setup. The scaling exponent from both experiments and
DNS agrees well with the geostrophic turbulence prediction. The prefactor of
the scaling-law is greater than the one diagnosed in previous idealized
numerical studies, pointing to an unexpected sensitivity of the heat transport
efficiency to the precise distribution of heat sources and sinks, which greatly
varies from planets to stars.


title: FD-SLAM: 3-D Reconstruction Using Features and Dense Matching

abstract:
  It is well known that visual SLAM systems based on dense matching are locally
accurate but are also susceptible to long-term drift and map corruption. In
contrast, feature matching methods can achieve greater long-term consistency
but can suffer from inaccurate local pose estimation when feature information
is sparse. Based on these observations, we propose an RGB-D SLAM system that
leverages the advantages of both approaches: using dense frame-to-model
odometry to build accurate sub-maps and on-the-fly feature-based matching
across sub-maps for global map optimisation. In addition, we incorporate a
learning-based loop closure component based on 3-D features which further
stabilises map building. We have evaluated the approach on indoor sequences
from public datasets, and the results show that it performs on par or better
than state-of-the-art systems in terms of map reconstruction quality and pose
estimation. The approach can also scale to large scenes where other systems
often fail.


title: The pion-nucleon sigma term from Lattice QCD

abstract:
  We summarize recent evidence, both from lattice QCD and chiral perturbation
theory, that suggests that larger-than-expected excited-state contamination
could be the reason for the tension between phenomenological determinations and
previous direct lattice-QCD calculations of the pion--nucleon sigma term
$\sigma_{\pi N}$. In addition, we extend the $\chi$PT analysis by calculating
the corrections due to including the $\Delta(1232)$ resonance as an explicit
degree of freedom. This correction is found to be small, thereby corroborating
the excited-state effects found in the $\Delta$-less calculation and the result
for $\sigma_{\pi N}$.


title:
Complex manifolds of Sobolev mappings and a Hartogs-type theorem in loop
  spaces

abstract:
  We recall the complex structure on the generalised loop spaces
$W^{k,2}(S,X)$, where $S$ is a compact real manifold with boundary and $X$ is a
complex manifold, and prove a Hartogs-type extension theorem for holomorphic
maps from certain domains in generalised loop spaces.


title: Derived categories of hearts on Kuznetsov components

abstract:
  We prove a general criterion which guarantees that an admissible subcategory
$\mathcal{K}$ of the derived category of an abelian category is equivalent to
the bounded derived category of the heart of a bounded t-structure. As a
consequence, we show that $\mathcal{K}$ has a strongly unique dg enhancement,
applying the recent results of Canonaco, Neeman and Stellari. We apply this
criterion to the Kuznetsov component $\mathop{\mathcal{K}u}(X)$ when $X$ is a
cubic fourfold, a Gushel--Mukai variety or a quartic double solid. In
particular, we obtain that these Kuznetsov components have strongly unique dg
enhancement and that exact equivalences of the form $\mathop{\mathcal{K}u}(X)
\xrightarrow{\sim} \mathop{\mathcal{K}u}(X')$ are of Fourier--Mukai type when
$X$, $X'$ belong to these classes of varieties, as predicted by a conjecture of
Kuznetsov.


title:
Intelligent Masking: Deep Q-Learning for Context Encoding in Medical
  Image Analysis

abstract:
  The need for a large amount of labeled data in the supervised setting has led
recent studies to utilize self-supervised learning to pre-train deep neural
networks using unlabeled data. Many self-supervised training strategies have
been investigated especially for medical datasets to leverage the information
available in the much fewer unlabeled data. One of the fundamental strategies
in image-based self-supervision is context prediction. In this approach, a
model is trained to reconstruct the contents of an arbitrary missing region of
an image based on its surroundings. However, the existing methods adopt a
random and blind masking approach by focusing uniformly on all regions of the
images. This approach results in a lot of unnecessary network updates that
cause the model to forget the rich extracted features. In this work, we develop
a novel self-supervised approach that occludes targeted regions to improve the
pre-training procedure. To this end, we propose a reinforcement learning-based
agent which learns to intelligently mask input images through deep Q-learning.
We show that training the agent against the prediction model can significantly
improve the semantic features extracted for downstream classification tasks. We
perform our experiments on two public datasets for diagnosing breast cancer in
the ultrasound images and detecting lower-grade glioma with MR images. In our
experiments, we show that our novel masking strategy advances the learned
features according to the performance on the classification task in terms of
accuracy, macro F1, and AUROC.


title:
Transfer matrix formulation of stationary scattering in 2D and 3D: A
  concise review of recent developments

abstract:
  We review a recently developed transfer matrix formulation of the stationary
scattering in two and three dimensions where the transfer matrix is a linear
operator acting in an infinite-dimensional function space. We discuss its
utility in circumventing the ultraviolet divergences one encounters in solving
the Lippman-Schwinger equation for delta-function potentials in two and three
dimensions. We also use it to construct complex scattering potentials
displaying perfect omnidirectional invisibility for frequencies below a freely
preassigned cutoff.


title: Data Selection Curriculum for Neural Machine Translation

abstract:
  Neural Machine Translation (NMT) models are typically trained on
heterogeneous data that are concatenated and randomly shuffled. However, not
all of the training data are equally useful to the model. Curriculum training
aims to present the data to the NMT models in a meaningful order. In this work,
we introduce a two-stage curriculum training framework for NMT where we
fine-tune a base NMT model on subsets of data, selected by both deterministic
scoring using pre-trained methods and online scoring that considers prediction
scores of the emerging NMT model. Through comprehensive experiments on six
language pairs comprising low- and high-resource languages from WMT'21, we have
shown that our curriculum strategies consistently demonstrate better quality
(up to +2.2 BLEU improvement) and faster convergence (approximately 50% fewer
updates).


title: Self-supervised Semantic Segmentation Grounded in Visual Concepts

abstract:
  Unsupervised semantic segmentation requires assigning a label to every pixel
without any human annotations. Despite recent advances in self-supervised
representation learning for individual images, unsupervised semantic
segmentation with pixel-level representations is still a challenging task and
remains underexplored. In this work, we propose a self-supervised pixel
representation learning method for semantic segmentation by using visual
concepts (i.e., groups of pixels with semantic meanings, such as parts,
objects, and scenes) extracted from images. To guide self-supervised learning,
we leverage three types of relationships between pixels and concepts, including
the relationships between pixels and local concepts, local and global concepts,
as well as the co-occurrence of concepts. We evaluate the learned pixel
embeddings and visual concepts on three datasets, including PASCAL VOC 2012,
COCO 2017, and DAVIS 2017. Our results show that the proposed method gains
consistent and substantial improvements over recent unsupervised semantic
segmentation approaches, and also demonstrate that visual concepts can reveal
insights into image datasets.


title:
Vibrational entropy of crystalline solids from covariance of atomic
  displacements

abstract:
  The vibrational entropy of a solid at finite temperature is investigated from
the perspective of information theory. Ab initio molecular dynamics (AIMD)
simulations generate ensembles of atomic configurations at finite temperature
from which we obtain the $N$-body distribution of atomic displacements,
$\rho_N$. We calculate the information-theoretic entropy from the expectation
value of $\ln{\rho_N}$. At a first level of approximation, treating individual
atomic displacements independently, our method may be applied using
Debye-Waller B-factors, allowing diffraction experiments to obtain an upper
bound on the thermodynamic entropy. At the next level of approximation we
correct the overestimation through inclusion of displacement covariances. We
apply this approach to elemental body-centered cubic sodium and face-centered
cubic aluminum, showing good agreement with experimental values above the Debye
temperatures of the metals. Below the Debye temperatures we extract an
effective vibrational density of states from eigenvalues of the covariance
matrix, and then evaluate the entropy quantum mechanically, again yielding good
agreement with experiment down to low temperatures. Our method readily
generalizes to complex solids, as we demonstrate for a high entropy alloy.
Further, our method applies in cases where the quasiharmonic approximation
fails, as we demonstrate by calculating the HCP/BCC transition in Ti.


title: A simple mnemonic to compute sums of powers

abstract:
  We give a simple recursive formula to obtain the general sum of the first $N$
natural numbers to the $r$th power. Our method allows one to obtain the general
formula for the $(r+1)$th power once one knows the general formula for the
$r$th power. The method is very simple to remember owing to an analogy with
differentiation and integration. Unlike previously known methods, no knowledge
of additional specific constants (such as the Bernoulli numbers) is needed.
This makes it particularly suitable for applications in cases when one cannot
consult external references, for example mathematics competitions.


title: Patterns for Documenting Open Source Frameworks

abstract:
  Documenting frameworks provides its users and maintainers useful information
on that software's architecture, design, and customization. Despite
documentation's importance, the process of creating and maintaining it is
considered to imply considerable effort, to be tedious, and expensive.
  In this work, we mine patterns from open source frameworks to uncover good
solutions used to document them that had not yet been described as patterns.
This process resulted in four new patterns.
  "Contribution Guidelines" helps developers to become contributors to a
project, helping them follow the good practices that have been adopted by its
maintainers. "Documentation Versioning" consists of having separate
documentation for older versions of the framework, to answer needs of the users
on such versions. "Migration Handbook" helps users migrating from previous
versions of the framework to newer ones. "Multi-language Support" allows
translated documents in several languages to support a wider range of users for
the framework.


title:
Dimension reduction of dynamical systems on networks with leading and
  non-leading eigenvectors of adjacency matrices

abstract:
  Dimension reduction techniques for dynamical systems on networks are
considered to promote our understanding of the original high-dimensional
dynamics. One strategy of dimension reduction is to derive a low-dimensional
dynamical system whose behavior approximates the observables of the original
dynamical system that are weighted linear summations of the state variables at
the different nodes. Recently proposed methods use the leading eigenvector of
the adjacency matrix of the network as the mixture weights to obtain such
observables. In the present study, we explore performances of this type of
one-dimensional reductions of dynamical systems on networks when we use
non-leading eigenvectors of the adjacency matrix as the mixture weights. Our
theory predicts that non-leading eigenvectors can be more efficient than the
leading eigenvector and enables us to select the eigenvector minimizing the
error. We numerically verify that the optimal non-leading eigenvector
outperforms the leading eigenvector for some dynamical systems and networks. We
also argue that, despite our theory, it is practically better to use the
leading eigenvector as the mixture weights to avoid misplacing the bifurcation
point too distantly and to be resistant against dynamical noise.


title: Improved higher-order Sobolev inequalities on CR sphere

abstract:
  We improve higher-order CR Sobolev inequalities on $S^{2n+1}$ under the
vanishing of higher order moments of the volume element. As an application, we
give a new and direct proof of the classification of minimizers of the CR
invariant higher-order Sobolev inequalities. In the same spirit, we prove
almost sharp Sobolev inequalities for GJMS operators to general CR manifolds,
and obtain the existence of minimizers in $C^{2k}(N)$ of higher-order CR
Yamabe-type problems when $Y_k(N)<Y_k(\mathbb{H}^n)$.


title:
Non-linear current and dynamical quantum phase transitions in the
  flux-quenched Su-Schrieffer-Heeger model

abstract:
  We investigate the dynamical effects of a magnetic flux quench in the
Su-Schrieffer-Heeger model in a one-dimensional ring geometry. We show that,
even when the system is initially in the half-filled insulating state, the flux
quench induces a time-dependent current that eventually reaches a finite
stationary value. Such persistent current, which exists also in the
thermodynamic limit, cannot be captured by the linear response theory and is
the hallmark of nonlinear dynamical effects occurring in the presence of
dimerization. Moreover, we show that, for a range of values of dimerization
strength and initial flux, the system exhibits dynamical quantum phase
transitions, despite the quench is performed within the same topological class
of the model.


title:
Self-supervised machine learning model for analysis of nanowire
  morphologies from transmission electron microscopy images

abstract:
  In the field of soft materials, microscopy is the first and often only
accessible method for structural characterization. There is a growing interest
in the development of machine learning methods that can automate the analysis
and interpretation of microscopy images. Typically training of machine learning
models require large numbers of images with associated structural labels,
however, manual labeling of images requires domain knowledge and is prone to
human error and subjectivity. To overcome these limitations, we present a
self-supervised transfer learning approach that uses a small number of labeled
microscopy images for training and performs as effectively as methods trained
on significantly larger data sets. Specifically, we train an image encoder with
unlabeled images and use that encoder for transfer learning of different
downstream image tasks (classification and segmentation) with a minimal number
of labeled images for training.

categories: cond-mat.mtrl-sci cond-mat.soft cs.CV cs.LG eess.IV


title:
Mapping out the thermodynamic stability of a QCD equation of state with
  a critical point using active learning

abstract:
  The Beam Energy Scan Theory (BEST) collaboration's equation of state (EoS)
incorporates a 3D Ising model critical point into the Quantum Chromodynamics
(QCD) equation of state from lattice simulations. However, it contains 4 free
parameters related to the size and location of the critical region in the QCD
phase diagram. Certain combinations of the free parameters lead to acausal or
unstable realizations of the EoS that should not be considered. In this work,
we use an active learning framework to rule out pathological EoS efficiently.
We find that checking stability and causality for a small portion of the
parameters' range is sufficient to construct algorithms that perform with
$>$96% accuracy across the entire parameter space. Though in this work we focus
on a specific case, our approach can be generalized to any EoS containing a
parameter space-class correspondence.


title:
Focused Jump-and-Repair Constraint Handling for Fixed-Parameter
  Tractable Graph Problems

abstract:
  Repair operators are often used for constraint handling in constrained
combinatorial optimization. We investigate the (1+1) EA equipped with a
tailored jump-and-repair operation that can be used to probabilistically repair
infeasible offspring in graph problems. Instead of evolving candidate solutions
to the entire graph, we expand the genotype to allow the (1+1) EA to develop in
parallel a feasible solution together with a growing subset of the instance (an
induced subgraph). With this approach, we prove that the EA is able to
probabilistically simulate an iterative compression process used in classical
fixed-parameter algorithmics to obtain a randomized FPT performance guarantee
on three NP-hard graph problems. For $k$-VertexCover, we prove that the (1+1)
EA using focused jump-and-repair can find a $k$-cover (if one exists) in $O(2^k
n^2\log n)$ iterations in expectation. This leads to an exponential (in $k$)
improvement over the best-known parameterized bound for evolutionary algorithms
on VertexCover. For the $k$-FeedbackVertexSet problem in tournaments, we prove
that the EA finds a feasible feedback set in $O(2^kk!n^2\log n)$ iterations in
expectation, and for OddCycleTransversal, we prove the optimization time for
the EA is $O(3^k k m n^2\log n)$. For the latter two problems, this constitutes
the first parameterized result for any evolutionary algorithm. We discuss how
to generalize the framework to other parameterized graph problems closed under
induced subgraphs and report experimental results that illustrate the behavior
of the algorithm on a concrete instance class.


title:
Assessing the impacts of decomposing a monolithic application for
  microservices: A case study

abstract:
  Monolithic applications are being decomposed into microservices
architectures, aiming to improve maintainability, performance, and
modularization. Although such decompositions have been widely performed in the
software industry today, little is reported in the literature about the impacts
of these decompositions. This work, therefore, reports a case study carried out
to investigate the impacts of decomposing a real industry application for
microservices architecture. The target application of the study refers to a
withdrawal operation, carried out by a financial institution, which was
extracted from a monolithic application to an application based on
microservices. In particular, metrics were applied to the monolithic and
microservices-based applications, aiming to quantify coupling, cohesion, CPU
consumption, and memory consumption. The results obtained indicate that the
microservices architecture generated better results for the software modularity
metrics, in addition to lower memory and CPU consumption. Finally, this work
brings reflection and points to challenges and future research directions that
need to be explored by academia and industry.


title:
On the origin of "patchy" energy conversion in electron diffusion
  regions

abstract:
  During magnetic reconnection, field lines interconnect in electron diffusion
regions (EDRs). In some EDRs the reconnection and energy conversion rates are
controlled by a steady out-of-plane electric field. In other EDRs the energy
conversion rate $\vec{J}\cdot\vec{E}'$ is "patchy", with electron-scale
large-amplitude positive and negative peaks. We investigate 22 EDRs observed by
NASA's Magnetospheric Multiscale (MMS) mission in a wide range of conditions to
determine the cause of patchy $\vec{J}\cdot\vec{E}'$. The patchiness of the
energy conversion is quantified and correlated with seven parameters describing
various aspects of the asymptotic inflow regions that affect the structure,
stability, and efficiency of reconnection. We find that (1) neither the guide
field strength nor the asymmetries in the inflow ion pressure, electron
pressure, reconnecting magnetic field strength, and number density are well
correlated with the patchiness of the EDR energy conversion, (2) the
out-of-plane axes of the 22 EDRs are typically fairly well aligned with the
"preferred" axes, which bisect the time-averaged inflow magnetic fields and
maximize the reconnection rate, and (3) the time-variability in the upstream
magnetic field direction is best correlated with the patchiness of the EDR
$\vec{J}\cdot\vec{E}'$. A 3-d fully-kinetic simulation of reconnection with a
non-uniform inflow magnetic field is analyzed; the variation in the magnetic
field generates secondary X-lines, which develop to maximize the reconnection
rate for the time-varying inflow magnetic field. The results suggest that
magnetopause reconnection, for which the inflow magnetic field direction is
often highly variable, may commonly be patchy in space, at least at the
electron scale.


title: Reinforcement Learning with Action-Free Pre-Training from Videos

abstract:
  Recent unsupervised pre-training methods have shown to be effective on
language and vision domains by learning useful representations for multiple
downstream tasks. In this paper, we investigate if such unsupervised
pre-training methods can also be effective for vision-based reinforcement
learning (RL). To this end, we introduce a framework that learns
representations useful for understanding the dynamics via generative
pre-training on videos. Our framework consists of two phases: we pre-train an
action-free latent video prediction model, and then utilize the pre-trained
representations for efficiently learning action-conditional world models on
unseen environments. To incorporate additional action inputs during
fine-tuning, we introduce a new architecture that stacks an action-conditional
latent prediction model on top of the pre-trained action-free prediction model.
Moreover, for better exploration, we propose a video-based intrinsic bonus that
leverages pre-trained representations. We demonstrate that our framework
significantly improves both final performances and sample-efficiency of
vision-based RL in a variety of manipulation and locomotion tasks. Code is
available at https://github.com/younggyoseo/apv.


title: Numerical calculation of 3-pion Coulomb scattering using scalar QED

abstract:
  The Coulomb scattering between three charged pions is shown to be
perturbatively calculable using scalar QED for the relative momenta accessible
to high-energy experiments of particle collisions. For the triplet relative
momenta, $Q_3 \gtrsim 50$ MeV/$c$, the calculated three-pion correlation
function at $O(\alpha^2)$ is very similar to the commonly-used triple-product
of pair factors -- Riverside approximation. At lower $Q_3$, but above the
lowest existing experimental measurement of $\sim15$ MeV/$c$, terms of
$O(\alpha^3)$ or higher are needed to obtain a convincing description of
three-pion Coulomb interactions. This is in contrast to two-pion correlations,
for which the known exact solution is shown to be saturated by the lower orders
at comparable pair relative momenta.


title: Atiyah-Bott localization in equivariant Witt cohomology

abstract:
  Let $N$ be a normalizer of the diagonal torus $T_1\cong \mathbb{G}_m$ in
$\text{SL}_2$. We prove localization theorems for $\text{SL}_2^n$ and $N^n$ for
equivariant cohomology with coefficients in the (twisted) Witt sheaf, along the
lines of the classical Atiyah-Bott localization theorems for equivariant
cohomology for a torus action. We also have an analog of the Bott residue
formula for $\text{SL}_2^n$ and $N$. In the case of an $\text{SL}_2^n$-action,
there is a rather serious restriction on the orbit type. For an $N$-action,
there is no restriction for the localization result, but for the Bott residue
theorem, one requires a certain type of decomposition of the fixed points for
the $T_1$-action.


title:
Multi-modal Misinformation Detection: Approaches, Challenges and
  Opportunities

abstract:
  As social media platforms are evolving from text-based forums into
multi-modal environments, the nature of misinformation in social media is also
changing accordingly. Taking advantage of the fact that visual modalities such
as images and videos are more favorable and attractive to the users, and
textual contents are sometimes skimmed carelessly, misinformation spreaders
have recently targeted contextual correlations between modalities e.g., text
and image. Thus, many research efforts have been put into development of
automatic techniques for detecting possible cross-modal discordances in
web-based media. In this work, we aim to analyze, categorize and identify
existing approaches in addition to challenges and shortcomings they face in
order to unearth new opportunities in furthering the research in the field of
multi-modal misinformation detection.


title:
A Conservative Q-Learning approach for handling distribution shift in
  sepsis treatment strategies

abstract:
  Sepsis is a leading cause of mortality and its treatment is very expensive.
Sepsis treatment is also very challenging because there is no consensus on what
interventions work best and different patients respond very differently to the
same treatment. Deep Reinforcement Learning methods can be used to come up with
optimal policies for treatment strategies mirroring physician actions. In the
healthcare scenario, the available data is mostly collected offline with no
interaction with the environment, which necessitates the use of offline RL
techniques. The Offline RL paradigm suffers from action distribution shifts
which in turn negatively affects learning an optimal policy for the treatment.
In this work, a Conservative-Q Learning (CQL) algorithm is used to mitigate
this shift and its corresponding policy reaches closer to the physicians policy
than conventional deep Q Learning. The policy learned could help clinicians in
Intensive Care Units to make better decisions while treating septic patients
and improve survival rate.


title: Affine Beilinson-Bernstein localization at the critical level

abstract:
  We prove the Frenkel-Gaitsgory localization conjecture describing regular
Kac-Moody representations at critical level via eigensheaves on the affine
Grassmannian using categorical Moy-Prasad theory. This extends previous work of
the authors.


title:
Predicting Peak Day and Peak Hour of Electricity Demand with Ensemble
  Machine Learning

abstract:
  Battery energy storage systems can be used for peak demand reduction in power
systems, leading to significant economic benefits. Two practical challenges are
1) accurately determining the peak load days and hours and 2) quantifying and
reducing uncertainties associated with the forecast in probabilistic risk
measures for dispatch decision-making. In this study, we develop a supervised
machine learning approach to generate 1) the probability of the next operation
day containing the peak hour of the month and 2) the probability of an hour to
be the peak hour of the day. Guidance is provided on the preparation and
augmentation of data as well as the selection of machine learning models and
decision-making thresholds. The proposed approach is applied to the Duke Energy
Progress system and successfully captures 69 peak days out of 72 testing months
with a 3% exceedance probability threshold. On 90% of the peak days, the actual
peak hour is among the 2 hours with the highest probabilities.


title: Automatic Debiased Machine Learning for Dynamic Treatment Effects

abstract:
  We extend the idea of automated debiased machine learning to the dynamic
treatment regime. We show that the multiply robust formula for the dynamic
treatment regime with discrete treatments can be re-stated in terms of a
recursive Riesz representer characterization of nested mean regressions. We
then apply a recursive Riesz representer estimation learning algorithm that
estimates de-biasing corrections without the need to characterize how the
correction terms look like, such as for instance, products of inverse
probability weighting terms, as is done in prior work on doubly robust
estimation in the dynamic regime. Our approach defines a sequence of loss
minimization problems, whose minimizers are the mulitpliers of the de-biasing
correction, hence circumventing the need for solving auxiliary propensity
models and directly optimizing for the mean squared error of the target
de-biasing correction.


title:
Whole Slide Image to DICOM Conversion as Event-Driven Cloud
  Infrastructure

abstract:
  The Digital Imaging and Communication in Medicine (DICOM) specification is
increasingly being adopted in digital pathology to promote data standardization
and interoperability. Efficient conversion of proprietary file formats into the
DICOM standard format is a key requirement for institutional adoption of DICOM,
necessary to ensure compatibility with existing scanners, microscopes, and data
archives. Here, we present a cloud computing architecture for DICOM conversion,
leveraging an event-driven microservices framework hosted in a serverless
computing environment in Google Cloud to enable efficient DICOM conversion at
scales ranging from individual images to institutional-scale datasets. In our
experiments, employing a microservices-based approach substantially reduced
runtime to process a batch of images relative to parallel and serial
processing. This work demonstrates the importance of designing scalable systems
for enabling enterprise-level adoption of digital pathology workflows, and
provides a blueprint for using a microservice architecture to enable efficient
DICOM conversion.