title:
Irreversible energy extraction from negative temperature two-dimensional
  turbulence

abstract:
  The formation and transition of patterns observed in various geophysical
turbulent flows are commonly explained in terms of statistical mechanics. This
study aims to extend the range of such a statistical mechanics theory by
introducing a perhaps novel conceptual model. We consider an inviscid
two-dimensional flow contained in a bounded domain, the shape of which is
distorted by an externally imposed force. Unlike the usual fixed boundary
cases, the flow energy within the domain is exchanged with the external system
via pressure work through the moving lateral boundary. Concurrently, the flow
field remains constrained by vorticity conservation. The vorticity equation
expanded onto time-dependent Laplacian eigenfunctions and subsequently
truncated at a finite number satisfies Liouville's theorem. Consequently, based
on energy and enstrophy constraints, a grand-canonical ensemble (GCE) is
defined with a negative or positive temperature. Beginning from the GCE, when
the domain shape is distorted from one shape to another in a finite time, the
Jarzynski equality is established. The pressure work performed on the system is
thus related to the difference in the Helmholtz free energy. The direction of
the inequality between the mean work and the free energy difference derived
from the Jarzynski equality depends on the sign of the initial temperature. If
the temperature is negative, the pressure work irreversibly extracts flow
energy from the system. This result provides new insights into the unique
thermodynamic characteristics of geophysical flows.


title: Discovery of a Quasar with Double-Peaked Broad Balmer Emission Lines

abstract:
  Most massive galaxies contain a supermassive black hole (SMBH) at their
center. When galaxies merge, their SMBHs sink to the center of the new galaxy
where they are thought to eventually merge. During this process an SMBH binary
is formed. The presence of two sets of broad emission lines in the optical
spectrum of an active galactic nucleus (AGN) has been interpreted as evidence
for two broad line regions (BLR), one surrounding each SMBH in a binary. We
modeled the broad Balmer emission lines in SDSS spectra of 373 extreme
variability AGNs using one broad and several narrow Gaussian components. We
report on the discovery of SDSS J021647.53$-$011341.5 (hereafter J0216) as a
double-peaked broad emission line source. Among the 373 AGNs there were five
sources that are known double-peaked emission line sources. Three of these have
been reported as candidate SMBH binaries in previous studies. We present all
six objects and their double-peaked broad Balmer emission lines, and discuss
the implications for a tidal disruption event (TDE) interpretation of the
extreme variability assuming the double-peaked sources are SMBH binaries.


title:
Ab Initio Study of K${_3}$Cu${_3}$P${_2}$ Material for Photovoltaic
  Applications

abstract:
  Search for efficient materials for application in the fields of
optoelectronics and photovoltaics are active areas of research across the
world. The potential of compounds such as K${_3}$Cu${_3}$P${_2}$ is not yet
fully realized. Therefore, we perform the ab initio studies based on density
functional theory to investigate the structural, electronic, elastic, and
optical properties of K${_3}$Cu${_3}$P${_2}$. Ground state properties were
computed in three different scenarios, i.e: with spin-orbit coupling (SOC),
without spin-orbit coupling, and with Hubbard U parameter. Direct electronic
bandgaps of 1.338 eV, 1.323 eV and 1.673 eV were obtained for
K${_3}$Cu${_3}$P${_2}$ without SOC, K${_3}$Cu${_3}$P${_2}$ with SOC and
K${_3}$Cu${_3}$P${_2}$ with Hubbard U respectively. In all the cases, Cu-d
orbitals were dominant at the top of the valence band. The effect of SOC on the
K${_3}$Cu${_3}$P${_2}$ computed lattice constant and bandgap was insignificant.
The mechanical stability test indicated that K${_3}$Cu${_3}$P${_2}$ is
mechanically stable at zero pressure. The optical band gap was found to
increase by 0.635 eV when Hubbard U was taken into consideration. Generally,
the inclusion of the Hubbard U parameter in density functional theory improves
the predictions of the bandgap and optical properties.


title: Binary Lifter Evaluation

abstract:
  Binary rewriting gives software developers, consumers, attackers, and
defenders the ability to modify both their own and third-party software for
instrumentation, customization, optimization, and hardening. Unfortunately, the
practical limitations of binary rewriting tools are often glossed over in
academic publications in this field, making it unclear to users if and when
these tools will work in practice. This, among other challenges, has prohibited
the widespread adoption of binary rewriting tools. To address this shortcoming,
we collect eight popular binary rewriting tools and assess their generality
across a broad range of input binary classes and the functional reliability of
the resulting rewritten binaries. Additionally, we evaluate the performance of
the rewriting tools themselves as well as the rewritten binaries they produce.
We also identify features that are predictive of rewriting success and show
that a simple decision tree model trained on these features can accurately
predict whether a particular tool can rewrite a target binary.
  The goal of this broad evaluation is to provide a state of the practice for
binary rewriting tools. We hope our findings will inform potential users of
binary rewriting, support binary rewriting tool developers, and set a shared
context for future research in this area. The binary rewriters, our corpus of
3344 sample binaries, and the evaluation infrastructure itself are all freely
available as open-source software.


title:
Electron-phonon coupling constant and BCS ratios in (La,Nd)-H
  superhydride

abstract:
  Stoichiometric near room-temperature superconductors (i.e., $H_3S$ and
$LaH_{10}$) exhibit so high ground state upper critical field, $B_{c2}(0)>100
T$, that the magnetic phase diagram in these materials cannot be measured in
non-destructive experiments. However, recently, Semenok et al (arXiv2203.06500)
proposed the idea to explore full magnetic phase diagram in NRTS samples in
which the superconducting order parameter is suppressed by the magnetic element
doping. If the element is uniformly distributed in the material, then the
theory of the electron-phonon mediated superconductivity predicts the
suppression of the order parameter in three-dimensional s-wave superconductor.
Semenok et al (arXiv2203.06500) experimentally proved this idea by substituting
La with the magnetic rare earth Nd in the $La_{1-x}Nd_{x}H_{10-y}$. As a
result, $T_c$ in the $La_{1-x}Nd_{x}H_{10-y}$ (x=0.09) decreases to Tc~120 K,
and the upper critical field decreases to $B_{c2}(T=41 K)=55 T$. While the
exact hydrogen content should be further established in the
$La_{1-x}Nd_{x}H_{10-y}$ (because similar $T_c$ suppression was observed in
hydrogen deficient $LaH_{10-y}$ reported by Drozdov et al (2019 Nature 569
528)), a significant part of the full magnetic phase diagram for
$La_{1-x}Nd_{x}H_{10-y}$ (x=0.09) was measured. Here we analyzed reported
Semenok et al (arXiv2203.06500) data for $La_{1-x}Nd_{x}H_{10-y}$ (x=0.09)
compressed at P=180 GPa and deduced: (a) Debye temperature, $T_{\theta}=1156
K$; (b) the electron-phonon coupling constant, ${\lambda}_{e-ph}=1.65$; (c) the
ground state superconducting energy gap, ${\Delta}(0)=20.2 meV$; (d) the
gap-to-transition temperature ratio, $2{\Delta}(0)/k_BT_c=4.0$; and (e) the
relative jump in specific heat at $T_c$, ${\Delta}C/C=1.68$. The deduced values
indicate that $La_{1-x}Nd_{x}H_{10-y}$ (x=0.09; P=180 GPa) is moderately strong
coupled s-wave superconductor.


title: Grid Induced Minor Theorem for Graphs of Small Degree

abstract:
  A graph $H$ is an induced minor of a graph $G$ if $H$ can be obtained from
$G$ by vertex deletions and edge contractions. We show that there is a function
$f(k, d) = O(k^{10} + 2^{d^5})$ so that if a graph has treewidth at least $f(k,
d)$ and maximum degree at most $d$, then it contains a $k \times k$-grid as an
induced minor. This proves the conjecture of Aboulker, Adler, Kim, Sintiari,
and Trotignon [Eur. J. Comb., 98, 2021] that any graph with large treewidth and
bounded maximum degree contains a large wall or the line graph of a large wall
as an induced subgraph. It also implies that for any fixed planar graph $H$,
there is a subexponential time algorithm for maximum weight independent set on
$H$-induced-minor-free graphs.


title:
Carbon monoxide emission lines reveal an inverted atmosphere in the
  ultra hot Jupiter WASP-33 b and indicate an eastward hot spot

abstract:
  We report the first detection of CO emission lines at high spectral
resolution in the day-side infrared thermal spectrum of an exoplanet. These
emission lines, found in the atmosphere of the ultra hot Jupiter WASP-33 b,
provide unambiguous evidence of its thermal inversion layer. Using spectra from
the MMT Exoplanet Atmosphere Survey (MEASURE, $R\sim15,000$), covering pre- and
post-eclipse orbital phases ($0.33<\phi<0.73$), we performed a
cross-correlation analysis with 1D PHOENIX model atmospheres to detect CO at
S/N=7.9 at $v_{sys}=0.15^{+0.64}_{-0.65}$ km/s and $K_{p}=229.5^{+1.1}_{-1.0}$
km/s. However, using the framework of Cross-Correlation-to-log-Likelihood
mapping, we further find that the spectral line depths, as probed by the
scaling parameter, change with phase: the line contrast is larger after the
eclipse than before. We then use the general circulation model SPARC/MITgcm
post-processed by the 3D gCMCRT radiative transfer code and interpret this
variation as due to an eastward-shifted hot spot. Before the eclipse, when the
hot spot is facing Earth, the thermal profiles are shallower, leading to a
smaller line depth despite greater overall flux. After the eclipse, the western
part of the day-side is facing Earth, where the thermal profiles are much
steeper, leading to larger line depth despite less overall flux. We thus
demonstrate that even relatively moderate resolution spectra can be used to
understand the 3D nature of close-in exoplanets, if assessed within the
log-likelihood framework, and that resolution can be traded for photon
collecting power when the induced Doppler-shift is sufficiently large. We
highlight that CO in ultra hot Jupiters is a good probe of their thermal
structure and corresponding dynamics, and does not suffer from stellar activity
unlike some atomic species, such as iron, that also appear in the hot host star
spectrum.


title:
Facial Expression Recognition based on Multi-head Cross Attention
  Network

abstract:
  Facial expression in-the-wild is essential for various interactive computing
domains. In this paper, we proposed an extended version of DAN model to address
the VA estimation and facial expression challenges introduced in ABAW 2022. Our
method produced preliminary results of 0.44 of mean CCC value for the VA
estimation task, and 0.33 of the average F1 score for the expression
classification task.


title: Differential Assessment of Black-Box AI Agents

abstract:
  Much of the research on learning symbolic models of AI agents focuses on
agents with stationary models. This assumption fails to hold in settings where
the agent's capabilities may change as a result of learning, adaptation, or
other post-deployment modifications. Efficient assessment of agents in such
settings is critical for learning the true capabilities of an AI system and for
ensuring its safe usage. In this work, we propose a novel approach to
differentially assess black-box AI agents that have drifted from their
previously known models. As a starting point, we consider the fully observable
and deterministic setting. We leverage sparse observations of the drifted
agent's current behavior and knowledge of its initial model to generate an
active querying policy that selectively queries the agent and computes an
updated model of its functionality. Empirical evaluation shows that our
approach is much more efficient than re-learning the agent model from scratch.
We also show that the cost of differential assessment using our method is
proportional to the amount of drift in the agent's functionality.


title: MD-SLAM: Multi-cue Direct SLAM

abstract:
  Simultaneous Localization and Mapping (SLAM) systems are fundamental building
blocks for any autonomous robot navigating in unknown environments. The SLAM
implementation heavily depends on the sensor modality employed on the mobile
platform. For this reason, assumptions on the scene's structure are often made
to maximize estimation accuracy. This paper presents a novel direct 3D SLAM
pipeline that works independently for RGB-D and LiDAR sensors. Building upon
prior work on multi-cue photometric frame-to-frame alignment, our proposed
approach provides an easy-to-extend and generic SLAM system. Our pipeline
requires only minor adaptations within the projection model to handle different
sensor modalities. We couple a position tracking system with an
appearance-based relocalization mechanism that handles large loop closures.
Loop closures are validated by the same direct registration algorithm used for
odometry estimation. We present comparative experiments with state-of-the-art
approaches on publicly available benchmarks using RGB-D cameras and 3D LiDARs.
Our system performs well in heterogeneous datasets compared to other
sensor-specific methods while making no assumptions about the environment.
Finally, we release an open-source C++ implementation of our system.


title: Open-set Recognition via Augmentation-based Similarity Learning

abstract:
  The primary assumption of conventional supervised learning or classification
is that the test samples are drawn from the same distribution as the training
samples, which is called closed set learning or classification. In many
practical scenarios, this is not the case because there are unknowns or unseen
class samples in the test data, which is called the open set scenario, and the
unknowns need to be detected. This problem is referred to as the open set
recognition problem and is important in safety-critical applications. We
propose to detect unknowns (or unseen class samples) through learning pairwise
similarities. The proposed method works in two steps. It first learns a closed
set classifier using the seen classes that have appeared in training and then
learns how to compare seen classes with pseudo-unseen (automatically generated
unseen class samples). The pseudo-unseen generation is carried out by
performing distribution shifting augmentations on the seen or training samples.
We call our method OPG (Open set recognition based on Pseudo unseen data
Generation). The experimental evaluation shows that the learned
similarity-based features can successfully distinguish seen from unseen in
benchmark datasets for open set recognition.


title:
A Representation Separation Perspective to Correspondences-free
  Unsupervised 3D Point Cloud Registration

abstract:
  3D point cloud registration in remote sensing field has been greatly advanced
by deep learning based methods, where the rigid transformation is either
directly regressed from the two point clouds (correspondences-free approaches)
or computed from the learned correspondences (correspondences-based
approaches). Existing correspondences-free methods generally learn the holistic
representation of the entire point cloud, which is fragile for partial and
noisy point clouds. In this paper, we propose a correspondences-free
unsupervised point cloud registration (UPCR) method from the representation
separation perspective. First, we model the input point cloud as a combination
of pose-invariant representation and pose-related representation. Second, the
pose-related representation is used to learn the relative pose wrt a "latent
canonical shape" for the source and target point clouds respectively. Third,
the rigid transformation is obtained from the above two learned relative poses.
Our method not only filters out the disturbance in pose-invariant
representation but also is robust to partial-to-partial point clouds or noise.
Experiments on benchmark datasets demonstrate that our unsupervised method
achieves comparable if not better performance than state-of-the-art supervised
registration methods.


title: Token Dropping for Efficient BERT Pretraining

abstract:
  Transformer-based models generally allocate the same amount of computation
for each token in a given sequence. We develop a simple but effective "token
dropping" method to accelerate the pretraining of transformer models, such as
BERT, without degrading its performance on downstream tasks. In short, we drop
unimportant tokens starting from an intermediate layer in the model to make the
model focus on important tokens; the dropped tokens are later picked up by the
last layer of the model so that the model still produces full-length sequences.
We leverage the already built-in masked language modeling (MLM) loss to
identify unimportant tokens with practically no computational overhead. In our
experiments, this simple approach reduces the pretraining cost of BERT by 25%
while achieving similar overall fine-tuning performance on standard downstream
tasks.


title:
VRNet: Learning the Rectified Virtual Corresponding Points for 3D Point
  Cloud Registration

abstract:
  3D point cloud registration is fragile to outliers, which are labeled as the
points without corresponding points. To handle this problem, a widely adopted
strategy is to estimate the relative pose based only on some accurate
correspondences, which is achieved by building correspondences on the
identified inliers or by selecting reliable ones. However, these approaches are
usually complicated and time-consuming. By contrast, the virtual point-based
methods learn the virtual corresponding points (VCPs) for all source points
uniformly without distinguishing the outliers and the inliers. Although this
strategy is time-efficient, the learned VCPs usually exhibit serious collapse
degeneration due to insufficient supervision and the inherent distribution
limitation. In this paper, we propose to exploit the best of both worlds and
present a novel robust 3D point cloud registration framework. We follow the
idea of the virtual point-based methods but learn a new type of virtual points
called rectified virtual corresponding points (RCPs), which are defined as the
point set with the same shape as the source and with the same pose as the
target. Hence, a pair of consistent point clouds, i.e. source and RCPs, is
formed by rectifying VCPs to RCPs (VRNet), through which reliable
correspondences between source and RCPs can be accurately obtained. Since the
relative pose between source and RCPs is the same as the relative pose between
source and target, the input point clouds can be registered naturally.
Specifically, we first construct the initial VCPs by using an estimated soft
matching matrix to perform a weighted average on the target points. Then, we
design a correction-walk module to learn an offset to rectify VCPs to RCPs,
which effectively breaks the distribution limitation of VCPs. Finally, we
develop a hybrid loss function to enforce the shape and geometry structure
consistency ...


title:
The stationary horizon and semi-infinite geodesics in the directed
  landscape

abstract:
  The stationary horizon is a stochastic process consisting of coupled Brownian
motions, indexed by their real-valued drifts. It was recently constructed by
the first author as the scaling limit of the Busemann process for exponential
last-passage percolation. We show that the stationary horizon is invariant
under the KPZ fixed point and is the unique such invariant distribution in the
appropriate space. We use this result to show that the stationary horizon
describes the Busemann process of the directed landscape, across all directions
of growth. We show the existence of semi-infinite geodesics in the directed
landscape, simultaneously across all initial points and all directions. We show
that, as a function of the direction, the set of discontinuities in the
Busemann process is a countable dense set, denoted $\Xi$, and this is exactly
the set of directions in which not all geodesics coalesce. For directions $\xi
\in \Xi$, from every initial point $p \in \mathbb R^2$, there exist at least
two distinct geodesics from $p$ that eventually split. Across all initial
points, this creates two distinct families of geodesics, each of which has a
coalescing structure. This is analogous to the result for the exponential
corner growth model proved by Janjigian, Rassoul-Agha, and the second author,
as well as the result for Brownian last-passage percolation proved by the last
two authors. We use tools developed in the latter work to deal with the
continuum of initial points in the plane.


title:
A method to estimate the size of particles using the open source
  software OpenPTV

abstract:
  A method to obtain particle sizes from images that are used for particle
tracking velocimetry is proposed. This is an open source method, developed to
use together with the open source software OpenPTV. First, the analysis of
different factors that affect the estimation of the particle size is made.
Then, a transformation is proposed to estimate the sizes from information
obtained in OpenPTV. The method requires an extra calibration with a flat
target containing circles of different sizes, but if the calibration is
successful, the sizes of particles can be estimated reliable within certain
limits. For example, for solid of non-translucent particles, sizes can be
estimated with an error of maximum 10 %, as long as the particle diameter
correspond to at least three pixels width in the obtained images. For
translucent particles some extra assumptions will be needed in the
transformation.


title: Fractionalization of subsystem symmetries in two dimensions

abstract:
  The fractionalization of global symmetry charges is a striking hallmark of
topological quantum order. Here, we discuss the fractionalization of subsystem
symmetries in two-dimensional topological phases. In line with previous no-go
arguments, we show that subsystem symmetry fractionalization is not possible in
many cases due to the additional rigid geometric structure of the symmetries.
However, we identify a new mechanism that allows fractionalization, involving
global relations between macroscopically many symmetry generators. We find that
anyons can fractionalize such relations, meaning that the total charge carried
under all generators involved in the global relation is non-trivial, despite
the fact that these generators multiply to the identity. We first discuss the
general algebraic framework needed to characterize this new type of
fractionalization, and then explore this framework using a number of exactly
solvable models with $\mathbb{Z}_2$ topological order, including models having
line and fractal symmetries. These models all showcase another necessary
property of subsystem symmetry fractionalization: fractionalized anyons must
have restricted mobility when the symmetry is enforced, such that they are
confined to a single line or point in the case of line and fractal symmetries,
respectively. Looking forward, we expect that our identification of the
importance of global relations in fractionalization will hold significance for
the classification of phases with subsystem symmetries in all dimensions.


title:
H\"older regularity and Liouville properties for nonlinear elliptic
  inequalities with power-growth gradient terms

abstract:
  This note studies local integral gradient bounds for distributional solutions
of a large class of partial differential inequalities with diffusion in
divergence form and power-like first-order terms. The applications of these
estimates are two-fold. First, we show the (sharp) global H\"older regularity
of distributional semi-solutions to this class of diffusive PDEs with
first-order terms having supernatural growth and right-hand side in a suitable
Morrey class posed on a bounded and regular open set $\Omega$. Second, we
provide a new proof of entire Liouville properties for inequalities with
superlinear first-order terms without assuming any one-side bound on the
solution for the corresponding homogeneous partial differential inequalities.
We also discuss some extensions of the previous properties to problems arising
in sub-Riemannian geometry and also to partial differential inequalities posed
on noncompact complete Riemannian manifolds under appropriate area-growth
conditions of the geodesic spheres, providing new results in both these
directions. The methods rely on integral arguments and do not exploit maximum
and comparison principles.


title: Love might be a second-order phase transition

abstract:
  The hypothesis of the human brain operation in vicinity of a critical point
has been a matter of a hot debate in the recent years. The evidence for a
possibility of a naturally occurring phase transition across this critical
point was missing so far. Here we show that love might be an example of such
second-order phase transition. This hypothesis allows to describe both love at
first sight and love from liking or friendship.


title:
Exemplifying parametric timed specifications over signals with bounded
  behavior

abstract:
  Specifying properties can be challenging work. In this paper, we propose an
automated approach to exemplify properties given in the form of automata
extended with timing constraints and timing parameters, and that can also
encode constraints over real-valued signals. That is, given such a
specification and given an admissible automaton for each signal, we output
concrete runs exemplifying real (or impossible) runs for this specification.
Specifically, our method takes as input a specification, and a set of
admissible behaviors, all given as a subclass of rectangular hybrid automata,
namely timed automata extended with arbitrary clock rates, signal constraints,
and timing parameters. Our method then generates concrete runs exemplifying the
specification.


title: Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer

abstract:
  Recent studies on StyleGAN show high performance on artistic portrait
generation by transfer learning with limited data. In this paper, we explore
more challenging exemplar-based high-resolution portrait style transfer by
introducing a novel DualStyleGAN with flexible control of dual styles of the
original face domain and the extended artistic portrait domain. Different from
StyleGAN, DualStyleGAN provides a natural way of style transfer by
characterizing the content and style of a portrait with an intrinsic style path
and a new extrinsic style path, respectively. The delicately designed extrinsic
style path enables our model to modulate both the color and complex structural
styles hierarchically to precisely pastiche the style example. Furthermore, a
novel progressive fine-tuning scheme is introduced to smoothly transform the
generative space of the model to the target domain, even with the above
modifications on the network architecture. Experiments demonstrate the
superiority of DualStyleGAN over state-of-the-art methods in high-quality
portrait style transfer and flexible style control.


title:
BigDetection: A Large-scale Benchmark for Improved Object Detector
  Pre-training

abstract:
  Multiple datasets and open challenges for object detection have been
introduced in recent years. To build more general and powerful object detection
systems, in this paper, we construct a new large-scale benchmark termed
BigDetection. Our goal is to simply leverage the training data from existing
datasets (LVIS, OpenImages and Object365) with carefully designed principles,
and curate a larger dataset for improved detector pre-training. Specifically,
we generate a new taxonomy which unifies the heterogeneous label spaces from
different sources. Our BigDetection dataset has 600 object categories and
contains over 3.4M training images with 36M bounding boxes. It is much larger
in multiple dimensions than previous benchmarks, which offers both
opportunities and challenges. Extensive experiments demonstrate its validity as
a new benchmark for evaluating different object detection methods, and its
effectiveness as a pre-training dataset.


title: Global Tracking Transformers

abstract:
  We present a novel transformer-based architecture for global multi-object
tracking. Our network takes a short sequence of frames as input and produces
global trajectories for all objects. The core component is a global tracking
transformer that operates on objects from all frames in the sequence. The
transformer encodes object features from all frames, and uses trajectory
queries to group them into trajectories. The trajectory queries are object
features from a single frame and naturally produce unique trajectories. Our
global tracking transformer does not require intermediate pairwise grouping or
combinatorial association, and can be jointly trained with an object detector.
It achieves competitive performance on the popular MOT17 benchmark, with 75.3
MOTA and 59.1 HOTA. More importantly, our framework seamlessly integrates into
state-of-the-art large-vocabulary detectors to track any objects. Experiments
on the challenging TAO dataset show that our framework consistently improves
upon baselines that are based on pairwise association, outperforming published
works by a significant 7.7 tracking mAP. Code is available at
https://github.com/xingyizhou/GTR.


title:
Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient
  Dexterous Manipulation

abstract:
  Optimizing behaviors for dexterous manipulation has been a longstanding
challenge in robotics, with a variety of methods from model-based control to
model-free reinforcement learning having been previously explored in
literature. Perhaps one of the most powerful techniques to learn complex
manipulation strategies is imitation learning. However, collecting and learning
from demonstrations in dexterous manipulation is quite challenging. The
complex, high-dimensional action-space involved with multi-finger control often
leads to poor sample efficiency of learning-based methods. In this work, we
propose 'Dexterous Imitation Made Easy' (DIME) a new imitation learning
framework for dexterous manipulation. DIME only requires a single RGB camera to
observe a human operator and teleoperate our robotic hand. Once demonstrations
are collected, DIME employs standard imitation learning methods to train
dexterous manipulation policies. On both simulation and real robot benchmarks
we demonstrate that DIME can be used to solve complex, in-hand manipulation
tasks such as 'flipping', 'spinning', and 'rotating' objects with the Allegro
hand. Our framework along with pre-collected demonstrations is publicly
available at https://nyu-robot-learning.github.io/dime.


title:
Modified gravitational wave propagation with higher modes and its
  degeneracies with lensing

abstract:
  Low-energy alternatives to General Relativity (GR) generically modify the
phase of gravitational waves (GWs) during their propagation. As detector
sensitivities increase, it becomes key to understand how these modifications
affect the GW higher modes and to disentangle possible degeneracies with
astrophysical phenomena. We apply a general formalism -- the WKB approach --
for solving analytically wave propagation in the spatial domain with a modified
dispersion relation (MDR). We compare this WKB approach to applying a
stationary phase approximation (SPA) in the temporal domain with time delays
associated to the group or particle velocity. To this end, we extend the SPA to
generic signals with higher modes, keeping careful track of reference phases
and arrival times. We find that the WKB approach coincides with the SPA using
the group velocity, in agreement with the principles of wave propagation. We
then explore the degeneracies between a GW propagation with an MDR and a
strongly-lensed GW in GR, since the latter can introduce a
frequency-independent phase shift which is not degenerate with source
parameters in the presence of higher modes. We find that for a particular MDR
there is an exact degeneracy for wave propagation, unlike with the SPA for
particle propagation. For the other cases, we search for the values of the MDR
parameters that minimize the $\chi^2$ and conclude that strongly-lensed GR GWs
could be misinterpreted as GWs in modified gravity. Future MDR constraints with
higher mode GWs should include the possibility of frequency-independent phase
shifts, allowing for the identification of modified gravity and strong lensing
distortions at the same time.


title:
Video Instance Segmentation via Multi-scale Spatio-temporal Split
  Attention Transformer

abstract:
  State-of-the-art transformer-based video instance segmentation (VIS)
approaches typically utilize either single-scale spatio-temporal features or
per-frame multi-scale features during the attention computations. We argue that
such an attention computation ignores the multi-scale spatio-temporal feature
relationships that are crucial to tackle target appearance deformations in
videos. To address this issue, we propose a transformer-based VIS framework,
named MS-STS VIS, that comprises a novel multi-scale spatio-temporal split
(MS-STS) attention module in the encoder. The proposed MS-STS module
effectively captures spatio-temporal feature relationships at multiple scales
across frames in a video. We further introduce an attention block in the
decoder to enhance the temporal consistency of the detected instances in
different frames of a video. Moreover, an auxiliary discriminator is introduced
during training to ensure better foreground-background separability within the
multi-scale spatio-temporal feature space. We conduct extensive experiments on
two benchmarks: Youtube-VIS (2019 and 2021). Our MS-STS VIS achieves
state-of-the-art performance on both benchmarks. When using the ResNet50
backbone, our MS-STS achieves a mask AP of 50.1 %, outperforming the best
reported results in literature by 2.7 % and by 4.8 % at higher overlap
threshold of AP_75, while being comparable in model size and speed on
Youtube-VIS 2019 val. set. When using the Swin Transformer backbone, MS-STS VIS
achieves mask AP of 61.0 % on Youtube-VIS 2019 val. set. Our code and models
are available at https://github.com/OmkarThawakar/MSSTS-VIS.


title:
EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for
  Monocular Object Pose Estimation

abstract:
  Locating 3D objects from a single RGB image via Perspective-n-Points (PnP) is
a long-standing problem in computer vision. Driven by end-to-end deep learning,
recent studies suggest interpreting PnP as a differentiable layer, so that
2D-3D point correspondences can be partly learned by backpropagating the
gradient w.r.t. object pose. Yet, learning the entire set of unrestricted 2D-3D
points from scratch fails to converge with existing approaches, since the
deterministic pose is inherently non-differentiable. In this paper, we propose
the EPro-PnP, a probabilistic PnP layer for general end-to-end pose estimation,
which outputs a distribution of pose on the SE(3) manifold, essentially
bringing categorical Softmax to the continuous domain. The 2D-3D coordinates
and corresponding weights are treated as intermediate variables learned by
minimizing the KL divergence between the predicted and target pose
distribution. The underlying principle unifies the existing approaches and
resembles the attention mechanism. EPro-PnP significantly outperforms
competitive baselines, closing the gap between PnP-based method and the
task-specific leaders on the LineMOD 6DoF pose estimation and nuScenes 3D
object detection benchmarks.


title:
Fluctuation-dissipation relation for a Bose-Einstein condensate of
  photons

abstract:
  For equilibrium systems, the magnitude of thermal fluctuations is closely
linked to the dissipative response to external perturbations. This
fluctuation-dissipation relation has been described for material particles in a
wide range of fields. Here we experimentally probe the relation between the
number fluctuations and the response function for a Bose-Einstein condensate of
photons coupled to a dye reservoir, demonstrating the fluctuation-dissipation
relation for a quantum gas of light. The observed agreement of the scale factor
with the environment temperature directly confirms the thermal nature of the
optical condensate.

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


title:
Power Network Uniqueness and Synchronization Stability from a
  Higher-order Structure Perspective

abstract:
  Triadic subgraph analysis reveals the structural features in power networks
based on higher-order connectivity patterns. Power networks have a unique triad
significance profile (TSP) of the five unidirectional triadic subgraphs in
comparison with the scale-free, small-world and random networks. Notably, the
triadic closure has the highest significance in power networks. Thus, the
unique TSP can serve as a structural identifier to differentiate power networks
from other complex networks. Power networks form a network superfamily.
Furthermore, synthetic power networks based on the random growth model grow up
to be networks belonging to the superfamily with a fewer number of transmission
lines. The significance of triadic closures strongly correlates with the
construction cost measured by network redundancy. The trade off between the
synchronization stability and the construction cost leads to the power network
superfamily. The power network characterized by the unique TSP is the
consequence of the trade-off essentially. The uniqueness of the power network
superfamily tells an important fact that power networks.


title: Dynamics and Control of Bubble-Propelled Microrobots

abstract:
  Having the advantage of being relatively fast and powerful, as well as
readily fabricated, spherical bubble-propelled microrobots are particularly
well suited for applications such as cargo delivery, micromanipulation, and
biological or environmental remediation. However, there have been limited
examples of control and manipulation with these microrobots and few studies on
their dynamics. Here we investigate the bubble formation and dynamics of both
hemispherically coated Janus microrobots as well as GLAD "patchy" microrobots
which not only provide for an interesting comparison, but also exhibit useful
properties in their own right. Specifically, we find that the patchy
microrobots have a tendency to produce smaller bubbles and undergo smoother
motion, properties that are beneficial for applications such as precise
micro-manipulation, for example. We demonstrate manipulation and assemble of
passive spheres on a substrate as well as at an air-liquid interface. We also
characterize the propulsion and bubble formation of both types of microrobots
and find that previously proposed theories insufficiently describe their motion
and bubble bursting mechanism. Additionally, we observe that the microrobots,
which reside at the air-liquid interface, demonstrate positive gravitaxis
towards the droplet edges which we attribute to a torque resulting from
opposing downward and buoyant forces on the microrobot.


title:
Multi-platform Process Flow Models and Algorithms for Extraction and
  Documentation of Digital Forensic Evidence from Mobile Devices

abstract:
  The increasing need for the examination of evidence from mobile and portable
gadgets increases the essential need to establish dependable measures for the
investigation of these gadgets. Many differences exist while detailing the
requirement for the examination of each gadget, to help detectives and
examiners in guaranteeing that of any kind piece of evidence extracted/
collected from any mobile devices is well documented and the outcomes can be
repeatable, a reliable and well-documented investigation process must be
implemented if the results of the examination are to be repeatable and
defensible in courts of law. In this paper we developed a generic process flow
model for the extraction of digital evidence in mobile devices running on
android, Windows, iOs and Blackberry operating system. The research adopted
survey approach and extensive literature review a s means to collect data. The
models developed were validate through expert opinion. Results of this work can
guide solution developers in ensuring standardization of evidence extraction
tools for mobile devices.


title: Computing Optimal Location of Microphone for Improved Speech Recognition

abstract:
  It was shown in our earlier work that the measurement error in the microphone
position affected the room impulse response (RIR) which in turn affected the
single-channel close microphone and multi-channel distant microphone speech
recognition. In this paper, as an extension, we systematically study to
identify the optimal location of the microphone, given an approximate and hence
erroneous location of the microphone in 3D space. The primary idea is to use
Monte-Carlo technique to generate a large number of random microphone positions
around the erroneous microphone position and select the microphone position
that results in the best performance of a general purpose automatic speech
recognition (gp-asr). We experiment with clean and noisy speech and show that
the optimal location of the microphone is unique and is affected by noise.


title: Adaptive job and resource management for the growing quantum cloud

abstract:
  As the popularity of quantum computing continues to grow, efficient quantum
machine access over the cloud is critical to both academic and industry
researchers across the globe. And as cloud quantum computing demands increase
exponentially, the analysis of resource consumption and execution
characteristics are key to efficient management of jobs and resources at both
the vendor-end as well as the client-end. While the analysis and optimization
of job / resource consumption and management are popular in the classical HPC
domain, it is severely lacking for more nascent technology like quantum
computing. This paper proposes optimized adaptive job scheduling to the quantum
cloud taking note of primary characteristics such as queuing times and fidelity
trends across machines, as well as other characteristics such as quality of
service guarantees and machine calibration constraints. Key components of the
proposal include a) a prediction model which predicts fidelity trends across
machine based on compiled circuit features such as circuit depth and different
forms of errors, as well as b) queuing time prediction for each machine based
on execution time estimations. Overall, this proposal is evaluated on simulated
IBM machines across a diverse set of quantum applications and system loading
scenarios, and is able to reduce wait times by over 3x and improve fidelity by
over 40\% on specific usecases, when compared to traditional job schedulers.


title: Quantum Feature Selection

abstract:
  In machine learning, fewer features reduce model complexity. Carefully
assessing the influence of each input feature on the model quality is therefore
a crucial preprocessing step. We propose a novel feature selection algorithm
based on a quadratic unconstrained binary optimization (QUBO) problem, which
allows to select a specified number of features based on their importance and
redundancy. In contrast to iterative or greedy methods, our direct approach
yields higherquality solutions. QUBO problems are particularly interesting
because they can be solved on quantum hardware. To evaluate our proposed
algorithm, we conduct a series of numerical experiments using a classical
computer, a quantum gate computer and a quantum annealer. Our evaluation
compares our method to a range of standard methods on various benchmark
datasets. We observe competitive performance.


title: Interpretability of Neural Network With Physiological Mechanisms

abstract:
  Deep learning continues to play as a powerful state-of-art technique that has
achieved extraordinary accuracy levels in various domains of regression and
classification tasks, including images, video, signal, and natural language
data. The original goal of proposing the neural network model is to improve the
understanding of complex human brains using a mathematical expression approach.
However, recent deep learning techniques continue to lose the interpretations
of its functional process by being treated mostly as a black-box approximator.
To address this issue, such an AI model needs to be biological and
physiological realistic to incorporate a better understanding of human-machine
evolutionary intelligence. In this study, we compare neural networks and
biological circuits to discover the similarities and differences from various
perspective views. We further discuss the insights into how neural networks
learn from data by investigating human biological behaviors and understandable
justifications.


title: Precipitaion Nowcasting using Deep Neural Network

abstract:
  Precipitation nowcasting is of great importance for weather forecast users,
for activities ranging from outdoor activities and sports competitions to
airport traffic management. In contrast to long-term precipitation forecasts
which are traditionally obtained from numerical models, precipitation
nowcasting needs to be very fast. It is therefore more challenging to obtain
because of this time constraint. Recently, many machine learning based methods
had been proposed. We propose the use three popular deep learning models
(U-net, ConvLSTM and SVG-LP) trained on two-dimensional precipitation maps for
precipitation nowcasting. We proposed an algorithm for patch extraction to
obtain high resolution precipitation maps. We proposed a loss function to solve
the blurry image issue and to reduce the influence of zero value pixels in
precipitation maps.


title: String correlators on $\text{AdS}_3$: analytic structure and dual CFT

abstract:
  We continue our study of string correlators on Euclidean $\text{AdS}_3$ with
pure NS-NS flux. The worldsheet and spacetime correlators have a rich analytic
structure, which we analyse completely for genus 0 four-point functions. We
show that correlators exhibit a simple behaviour near their singularities. The
spacetime correlators are meromorphic functions in the
$\mathrm{SL}(2,\mathbb{R})$-spins, whose pole structure is shown to agree with
the prediction of a recent proposal for the dual $\text{CFT}_2$. Moreover, we
also compute the residues of the spacetime correlators for some of the poles
exactly and find again a perfect match with the proposal for the dual
$\text{CFT}_2$, thereby checking the duality for some non-trivial four-point
functions exactly. Our computations simplify drastically in the tensionless
limit of $\mathrm{AdS}_3 \times \mathrm{S}^3 \times \mathbb{T}^4$ where the
behaviour near the poles gives in fact the exact answer. This paper is the
third in a series with several installments.


title:
Hard probe path lengths and event-shape engineering of the quark-gluon
  plasma

abstract:
  As particles traverse the quark-gluon plasma (QGP) formed during a heavy ion
collision they undergo energy loss depending on the distance travelled. We
study several temperature- and velocity-weighted path length distributions of
non-interacting particles as they traverse the plasma using the Trajectum heavy
ion code, including those of back-to-back path lengths. We use event-shape
engineering (ESE) in combination with in-plane versus out-of-plane selection to
accurately control these path lengths. Lastly, we show how soft observables
depend on the different ESE classes.


title:
NuSTAR Observations of 52 Compton-thick Active Galactic Nuclei Selected
  by the Swift/BAT All-sky Hard X-Ray Survey

abstract:
  We present the systematic broadband X-ray spectral analysis of 52
Compton-thick ($24 \leq \log N_{\mathrm{H}}^{\mathrm{LOS}}/\mathrm{cm}^{-2}$)
active galactic nucleus (CTAGN) candidates selected by the Swift/BAT all-sky
hard X-ray survey observed with Chandra, XMM--Newton, Swift/XRT, Suzaku, and
NuSTAR. The XMM--Newton data of 10 objects and the NuSTAR data of 15 objects
are published for the first time. We use an X-ray spectral model from a clumpy
torus (XClumpy) to determine the torus properties. As a result, the hydrogen
column density along the line of sight $N_{\mathrm{H}}^{\mathrm{LOS}}$ obtained
from the XClumpy model indicate that 24 objects are Compton-thin AGNs and 28
objects are Compton-thick AGNs in the 90\% confidence interval. The main reason
is the difference in the torus model applied. The hydrogen column density along
the equatorial direction $N_{\mathrm{H}}^{\mathrm{Equ}}$ of CTAGNs inferred
from the XClumpy model is larger than that of less obscured AGNs. The
Compton-thin torus covering factor $C_{22}$ obtained from the XClumpy model is
consistent with that of Ricci et al. (2017) in the low Eddington ratio ($\log
R_{\mathrm{Edd}} \leq -1.0$), whereas $C_{22}$ inferred from the XClumpy model
is larger than that of Ricci et al. (2017) in the high Eddington ratio ($-1.0
\leq \log R_{\mathrm{Edd}}$). The average value of the Compton-thick torus
covering factor $C_{24}$ obtained from the XClumpy model is $36_{-4}^{+4}$\%.
This value is larger than that of Ricci et al. (2015) ($C_{24} \simeq
27_{-4}^{+4}$\%) based on the assumption that all AGNs have intrinsically the
same torus structure. These results suggest that the structure of CTAGN may be
intrinsically different from that of less obscured AGN.


title: Spin-induced scalarization and magnetic fields

abstract:
  In the presence of certain non-minimal couplings between a scalar field and
the Gauss-Bonnet curvature invariant, Kerr black holes can scalarize, as long
as they are spinning fast enough. This provides a distinctive violation of the
Kerr hypothesis, occurring only for some high spin range. In this paper we
assess if strong magnetic fields, that may exist in the vicinity of
astrophysical black holes, could facilitate this distinctive effect, by
bringing down the spin threshold for scalarization. This inquiry is motivated
by the fact that self-gravitating magnetic fields, by themselves, can also
promote "spin-induced" scalarization. Nonetheless, we show that in the
\textit{vicinity of the horizon} the effect of the magnetic field $B$ on a
black hole of mass $M$, up to $BM\lesssim 1$, works \textit{against}
spin-induced scalarization, requiring a larger dimensionless spin $j$ from the
black hole. A geometric interpretation for this result is suggested, in terms
of the effects of rotation $vs.$ magnetic fields on the horizon geometry.


title:
Observing the inner parsec-scale region of candidate neutrino-emitting
  blazars

abstract:
  Context. Many questions on the nature of astrophysical counterparts of
high-energy neutrinos remain unanswered. There is increasing evidence of a
connection between blazar jets and neutrino events, with the flare of the
gamma-ray blazar TXS0506+056 in spatial and temporal proximity of IC170922A
representing one of the most outstanding associations of high-energy neutrinos
with astrophysical sources reported so far. Aims. With the purpose of
characterising potential blazar counterparts to high-energy neutrinos, we
analysed the parsec-scale regions of gamma-ray blazars in spatial coincidence
with neutrinos detected by IceCube. Specifically, we intended to investigate
peculiar radio properties of the candidate counterparts related to neutrino
production, as radio flares coincident to the neutrino detection or features in
jets morphology (limb brightening, transverse structures). Methods. We
collected multi-frequency VLBI follow-up observations of candidate counterparts
of four high-energy neutrino events detected by IceCube between January 2019
and November 2020. We analysed their radio characteristics soon after the
neutrino arrival in comparison with archival VLBI observations and
low-frequency radio observations. We discussed our results with respect to
previous statistical works and studies on the case of TXS 0506+056. Results. We
identified and analysed in detail five potential neutrino emitting blazars. Our
results suggest an enhanced state of radio activity for one source,
PKS1725+123. However, the lack of adequate monitoring prior to the neutrino
events was a limitation in tracing radio activity and morphological changes in
all the sources. Conclusions. We suggest that PKS1725+123 is a promising
neutrino source candidate. For the other four sources, our results alone do not
allow us to reveal a strong connection between the radio activity state at
neutrino arrival.


title: Probing pre-BBN era with Scale Invarint FIMP

abstract:
  Detecting dark matter (DM) relic via freeze-in is difficult in laboratories
due to smallness of the couplings involved. However, a non-standard
cosmological history of the Universe, prior to Big Bang Nucleosynthesis (BBN),
can dramatically change this scenario. In this context, we study the freeze-in
production of dark matter in classically scale invariant $U(1)_X$ gauge
extension of the Standard Model (SM), recently dubbed as the \textit{Scale
Invariant FIMP Miracle}. We assume an additional species dominates the energy
density of the Universe at early times, causing the expansion rate at a given
temperature to be larger than that in the standard radiation-dominated case. We
find, the \textit{out-of-equilibrium} scattering processes involving particles
in the thermal bath lead to significantly suppressed DM production in this era,
thereby enhancing the couplings between the visible and the dark sector (by
several orders of magnitude) to satisfy the observed DM abundance, and
improving the detection prospects for freeze-in in turn. Scale invariance of
the underlying theory leaves only four free parameters in the model: the DM
mass $m_X$, the gauge coupling $g_X$, the temperature of transition $T_R$ from
early scalar-dominated to radiation-dominated era and the power-law dependence
$n$ of this temperature. We show, within this minimal set-up, experiments like
FASER, MATHUSLA, DUNE, SHiP will be probing various cosmological models
depending on the choice of $\{n,\,T_R\}$ that also satisfy the PLANCK observed
relic density bound. Moreover, due to the presence of a naturally light scalar
mediator, the direct detection of the DM at XENON1T, PandaX-4T or XENONnT
becomes relevant for Higgs-scalar mixing $\sin\theta\simeq\{10^{-5}-10^{-3}\}$,
thus providing complementary probes for freeze-in, as well as for non-standard
cosmological pre-BBN era.


title: Shoring Up the Foundations: Fusing Model Embeddings and Weak Supervision

abstract:
  Foundation models offer an exciting new paradigm for constructing models with
out-of-the-box embeddings and a few labeled examples. However, it is not clear
how to best apply foundation models without labeled data. A potential approach
is to fuse foundation models with weak supervision frameworks, which use weak
label sources -- pre-trained models, heuristics, crowd-workers -- to construct
pseudolabels. The challenge is building a combination that best exploits the
signal available in both foundation models and weak sources. We propose Liger,
a combination that uses foundation model embeddings to improve two crucial
elements of existing weak supervision techniques. First, we produce finer
estimates of weak source quality by partitioning the embedding space and
learning per-part source accuracies. Second, we improve source coverage by
extending source votes in embedding space. Despite the black-box nature of
foundation models, we prove results characterizing how our approach improves
performance and show that lift scales with the smoothness of label
distributions in embedding space. On six benchmark NLP and video tasks, Liger
outperforms vanilla weak supervision by 14.1 points, weakly-supervised kNN and
adapters by 11.8 points, and kNN and adapters supervised by traditional hand
labels by 7.2 points.


title:
Probing phases of quantum matter with an ion-trap tensor-network quantum
  eigensolver

abstract:
  Tensor-Network (TN) states are efficient parametric representations of ground
states of local quantum Hamiltonians extensively used in numerical simulations.
Here we encode a TN ansatz state directly into a quantum simulator, which can
potentially offer an exponential advantage over purely numerical simulation. In
particular, we demonstrate the optimization of a quantum-encoded TN ansatz
state using a variational quantum eigensolver on an ion-trap quantum computer
by preparing the ground states of the extended Su-Schrieffer-Heeger model. The
generated states are characterized by estimating the topological invariants,
verifying their topological order. Our TN encoding as a trapped ion circuit
employs only single-site addressing optical pulses - the native operations
naturally available on the platform. We reduce nearest-neighbor crosstalk by
selecting different magnetic sublevels with well-separated transition
frequencies to encode even and odd qubits.


title: Radiation reaction for spinning black-hole scattering

abstract:
  Starting from the leading soft term of the 5-point amplitude, involving a
graviton and two Kerr black holes, that factorises into the product of the
elastic amplitude without the graviton and the leading soft factor, we compute
the infrared divergent contribution to the imaginary part of the two-loop
eikonal. Then, using analyticity and crossing symmetry, we determine the
radiative contribution to the real part of the two-loop eikonal and from it the
radiative part of the deflection angle for spins aligned to the orbital angular
momentum, the loss of angular momentum and the zero frequency limit of the
energy spectrum for any spin and for any spin orientation. For spin one we find
perfect agreement with recent results obtained with the supersymmetric
worldline formalism.


title:
On Exploiting Layerwise Gradient Statistics for Effective Training of
  Deep Neural Networks

abstract:
Adam and AdaBelief compute and make use of elementwise adaptive stepsizes in
training deep neural networks (DNNs) by tracking the exponential moving average
(EMA) of the squared-gradient g_t^2 and the squared prediction error
(m_t-g_t)^2, respectively, where m_t is the first momentum at iteration t and
can be viewed as a prediction of g_t. In this work, we investigate if layerwise
gradient statistics can be expoited in Adam and AdaBelief to allow for more
effective training of DNNs. We address the above research question in two
steps. Firstly, we slightly modify Adam and AdaBelief by introducing layerwise
adaptive stepsizes in their update procedures via either pre- or
post-processing. Our empirical results indicate that the slight modification
produces comparable performance for training VGG and ResNet models over CIFAR10
and CIFAR100, suggesting that layer-wise gradient statistics play an important
role towards the success of Adam and AdaBelief for at least certian DNN tasks.
In the second step, we propose Aida, a new optimisation method, with the
objective that the elementwise stepsizes within each layer have significantly
smaller statistical variances, and the layerwise average stepsizes are much
more compact across all the layers. Motivated by the fact that (m_t-g_t)^2 in
AdaBelief is conservative in comparison to g_t^2 in Adam in terms of layerwise
statistical averages and variances, Aida is designed by tracking a more
conservative function of m_t and g_t than (m_t-g_t)^2 via layerwise vector
projections. Experimental results show that Aida produces either competitive or
better performance with respect to a number of existing methods including Adam
and AdaBelief for a set of challenging DNN tasks. Code is available at this URL


title: Evolution of Dynamical Signature in the X-cube Fracton Topological Order

abstract:
  As an unconventional realization of topological orders with an exotic
interplay of topology and geometry, fracton (topological) orders feature
subextensive topological ground state degeneracy and subdimensional excitations
that are movable only within a certain subspace. It has been known in exactly
solvable three-dimensional (3D) X-cube model that universally represents the
type-I fracton orders, mobility constraints on subdimensional excitations
originate from the absence of spatially deformable string-like operators. To
unveil the interplay of topology and geometry, in this paper, we study
dynamical signature in the X-cube model in the presence of external Zeeman
fields via large-scale quantum Monte Carlo simulation and stochastic analytic
continuation. We compute both real-space correlation functions and dynamic
structure factors of subdimensional excitations (i.e., fractons, lineons, and
planons) in the fracton phase and their evolution into the trivial paramagnetic
phase by increasing external fields. We find in the fracton phase, the
correlation functions and the spectral functions show clear anisotropy exactly
caused by the underlying mobility constraints. On the other hand, the external
fields successfully induce quantum fluctuations and offer mobility to
excitations along the subspace allowed by mobility constraints. These numerical
results provide the evolution of dynamical signature of subdimensional
particles in fracton orders, indicating that the mobility constraints on local
dynamical properties of subdimensional excitations are deeply related to the
existence of fracton topological order. The results will also be helpful in
potential experimental identifications in spectroscopy measurements such as
neutron scattering and nuclear magnetic resonance.


title:
The Dependence of Theoretical Synthetic Spectra on $\alpha$-enhancement
  in Young, Binary Stellar Populations

abstract:
  The enhancement of $\alpha$ elements such as oxygen is an important phase in
the chemical evolution of the early Universe, with nebular material becoming
enriched in these elements sooner than iron. Here we present models which
incorporate stellar spectra with $\alpha$-enhanced compositions, focusing on
the impact on the integrated light of young stellar populations, including
those with large binary star fractions using the Binary Populations and
Spectral Synthesis (BPASS) framework, while using Solar-scaled stellar
evolution models. We find that broad spectrum outputs such as production of
ionising flux, the ultraviolet spectral slope and optical colours are only
weakly affected by a change in [$\alpha$/Fe]. A number of features such as
ultraviolet line indices (e.g. at 1719 and 1853\r{A}) and optical line indices
(such as MgB) are sensitive to such changes in composition for a continuously
star-forming population and a single starburst population respectively. We find
that at ages of more than 1Gyr, $\alpha$-enhanced stellar populations appear
bluer than their Solar-scaled counterparts, and show expected sensitivity of
optical line indices to composition, in agreement with previous work. The
ultraviolet stellar absorption lines are relatively insensitive to subtleties
in the abundances ratios, although with sufficient measurement precision, a
combination of UV line indices may enable a simultaneous measurement of total
metallicity mass fraction and [$\alpha$/Fe] in young stellar populations. The
output models are designated as BPASS v2.3 and made available to the community
with the aim of assisting interpretation of observations of high-redshift
galaxies with the James Webb Space Telescope.


title: The Fundamental Plane Relation for Gamma-Ray Pulsars Implied by 4FGL

abstract:
  We explore the validity of the recently reported fundamental plane (FP)
relation of gamma-ray pulsars using 190 pulsars included in the latest 4FGL-DR3
catalog. This sample number is more than twice as large as that of the original
study. The FP relation incorporates 4 parameters, i.e., the spin-down power,
$\dot{\mathcal{E}}$, the surface magnetic field, $B_{\star}$, the total
gamma-ray luminosity, $L_{\gamma}$, and a spectral cutoff energy,
$\epsilon_{\rm cut}$. The derivation of $\epsilon_{\rm cut}$ is the most
intriguing one because $\epsilon_{\rm cut}$ depends on the proper
interpretation of the available phase-averaged spectra. We construct synthetic
phase-averaged spectra, guided by the few existing phase-resolved ones, to find
that the best fit cutoff energy, $\epsilon_{\rm c1}$, corresponding to a purely
exponential cutoff (plus a power law) spectral form, is the parameter that
optimally probes the maximum cutoff energy of the emission that originates from
the core of the dissipative region, i.e., the equatorial current sheet.
Computing this parameter for the 190 4FGL pulsars, we find that the resulting
FP relation, i.e. the gamma-ray luminosity in terms of the other observables,
reads $L_{\gamma}=10^{14.3\pm 1.3}(\epsilon_{\rm c1}/{\rm
MeV})^{1.39\pm0.17}(B_{\star}/{\rm G})^{0.12\pm 0.03}(\dot{\mathcal{E}}/{\rm
erg\;s^{-1}})^{0.35\pm 0.05}{\rm ~erg\;s^{-1}}$; this is in good agreement with
both the empirical relation reported by Kalapotharakos et al. (2019) and the
theoretically predicted relation for curvature radiation. Finally, we revisit
the radiation reaction limited condition, to find it is a sufficient but not
necessary condition for the theoretical derivation of the FP relation. However,
the assumption of the radiation reaction limited acceleration reveals the
underlying accelerating electric field component and its scaling with
$\dot{\mathcal{E}}$.


title: A Manifold View of Adversarial Risk

abstract:
  The adversarial risk of a machine learning model has been widely studied.
Most previous works assume that the data lies in the whole ambient space. We
propose to take a new angle and take the manifold assumption into
consideration. Assuming data lies in a manifold, we investigate two new types
of adversarial risk, the normal adversarial risk due to perturbation along
normal direction, and the in-manifold adversarial risk due to perturbation
within the manifold. We prove that the classic adversarial risk can be bounded
from both sides using the normal and in-manifold adversarial risks. We also
show with a surprisingly pessimistic case that the standard adversarial risk
can be nonzero even when both normal and in-manifold risks are zero. We
finalize the paper with empirical studies supporting our theoretical results.
Our results suggest the possibility of improving the robustness of a classifier
by only focusing on the normal adversarial risk.


title: Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis

abstract:
  While recent years have witnessed a dramatic upsurge of exploiting deep
neural networks toward solving image denoising, existing methods mostly rely on
simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG
compression noise and camera sensor noise, and a general-purpose blind
denoising method for real images remains unsolved. In this paper, we attempt to
solve this problem from the perspective of network architecture design and
training data synthesis. Specifically, for the network architecture design, we
propose a swin-conv block to incorporate the local modeling ability of residual
convolutional layer and non-local modeling ability of swin transformer block,
and then plug it as the main building block into the widely-used image-to-image
translation UNet architecture. For the training data synthesis, we design a
practical noise degradation model which takes into consideration different
kinds of noise (including Gaussian, Poisson, speckle, JPEG compression, and
processed camera sensor noises) and resizing, and also involves a random
shuffle strategy and a double degradation strategy. Extensive experiments on
AGWN removal and real image denoising demonstrate that the new network
architecture design achieves state-of-the-art performance and the new
degradation model can help to significantly improve the practicability. We
believe our work can provide useful insights into current denoising research.


title: Mode- and Space- Resolved Thermal Transport of Alloy Nanostructures

abstract:
  Nanostructured semiconducting alloys obtain ultra-low thermal conductivity as
a result of the scattering of phonons with a wide range of mean-free-paths
(MFPs). In these materials, long-MFP phonons are scattered at the nanoscale
boundaries whereas short-MFP high-frequency phonons are impeded by disordered
point defects introduced by alloying. While this trend has been validated by
simplified analytical and numerical methods, an ab-initio space-resolved
approach remains elusive. To fill this gap, we calculate the thermal
conductivity reduction in porous alloys by solving the mode-resolved Boltzmann
transport equation for phonons using the finite-volume approach. We analyze
different alloys, length-scales, concentrations, and temperatures, obtaining a
very large reduction in the thermal conductivity over the entire configuration
space. For example, a ~97% reduction is found for Al$_{0.8}$In$_{0.2}$As with
25% porosity. Furthermore, we employ these simulations to validate our recently
introduced "Ballistic Correction Model" (BCM), an approach that estimates the
effective thermal conductivity using the characteristic MFP of the bulk alloy
and the length-scale of the material. The BCM is then used to provide guiding
principles in designing alloy-based nanostructures. Notably, it elucidates how
porous alloys such as Si$_{x}$Ge$_{1-x}$ obtain larger thermal conductivity
reduction compared to porous Si or Ge, while also explaining why we should not
expect similar behavior in alloys such as Al$_{x}$In$_{1-x}$As. By taking into
account the synergy from scattering at different scales, we provide a route for
the design of materials with ultra-low thermal conductivity.


title: Inhomogeneity in the Local ISM and its Relation to the Heliosphere

abstract:
  This paper reviews past research and new studies underway of the local
interstellar environment and its changing influence on the heliosphere. The
size, shape, and physical properties of the heliosphere outside of the
heliopause are determined by the surrounding environment - now the outer region
of the Local Interstellar Cloud (LIC). Analysis of high-resolution HST spectra
led to a kinematic model with many interstellar clouds. This analysis
identified fifteen clouds located within about 10 pc of the Sun and their mean
temperatures, turbulence, and velocity vectors. With the increasing number of
sight lines now being analyzed, we find that temperatures and turbulent
velocities have spatial variations within the LIC and other nearby clouds much
larger than measurement uncertainties, and that these spatial variations appear
to be randomly distributed and can be fit by Gaussians. The inhomogeneous
length scale is less than 4,000 AU, a distance that the heliosphere will
traverse in less than 600 years. The temperatures and turbulent velocities do
not show significant trends with stellar distance or angle from the LIC center.
If/when the Sun enters an inter-cloud medium, the physical properties of the
future heliosphere will be very different from the present. For the heliosheath
and the very local interstellar medium (VLISM) just outside of the heliopause,
the total pressures are approximately equal to the gravitational pressure of
overlying material in the Galaxy. The internal pressure in the LIC is far below
that in the VLISM, but there is an uncertain ram pressure term produced by the
flow of the LIC with respect to its environment.


title:
Effectively leveraging Multi-modal Features for Movie Genre
  Classification

abstract:
  Movie genre classification has been widely studied in recent years due to its
various applications in video editing, summarization, and recommendation. Prior
work has typically addressed this task by predicting genres based solely on the
visual content. As a result, predictions from these methods often perform
poorly for genres such as documentary or musical, since non-visual modalities
like audio or language play an important role in correctly classifying these
genres. In addition, the analysis of long videos at frame level is always
associated with high computational cost and makes the prediction less
efficient. To address these two issues, we propose a Multi-Modal approach
leveraging shot information, MMShot, to classify video genres in an efficient
and effective way. We evaluate our method on MovieNet and Condensed Movies for
genre classification, achieving 17% ~ 21% improvement on mean Average Precision
(mAP) over the state-of-the-art. Extensive experiments are conducted to
demonstrate the ability of MMShot for long video analysis and uncover the
correlations between genres and multiple movie elements. We also demonstrate
our approach's ability to generalize by evaluating the scene boundary detection
task, achieving 1.1% improvement on Average Precision (AP) over the
state-of-the-art.


title:
Dynamically Avoiding Amorphous Obstacles with Topological Manifold
  Learning and Deep Autoencoding

abstract:
  To achieve conflict-free human-machine collaborations, robotic agents need to
skillfully avoid continuously moving obstacles while achieving collective
objectives. Sometimes, these obstacles can even change their 3D shapes and
forms simultaneously, hence being "amorphous". To this end, this paper
formulates the problem of Dynamic Amorphous Obstacle Avoidance (DAO-A), where a
robotic arm can dexterously avoid dynamically generated obstacles that
constantly change their trajectories and their 3D forms. Specifically, we
introduce a novel control strategy for robotic arms that leverages both
topological manifold learning and latest deep learning advancements. We test
our learning framework, using a 7-DoF robotic manipulator, in both simulation
and physical experiments, where the robot satisfactorily learns and synthesizes
realistic skills avoiding previously-unseen obstacles, while generating novel
movements to achieve predefined motion objectives. Most notably, our learned
methodology, once finalized, for a given robotic manipulator, can avoid any
number of 3D obstacles with arbitrary and unseen moving trajectories, therefore
it is universal, versatile, and completely reusable. Complete video
demonstrations of our experiments can be found in
https://sites.google.com/view/daoa/home.


title:
On the Fast Direct Solution of a Preconditioned Electromagnetic Integral
  Equation

abstract:
  This work presents a fast direct solver strategy for electromagnetic integral
equations in the high-frequency regime. The new scheme relies on a suitably
preconditioned combined field formulation and results in a single skeleton form
plus identity equation. This is obtained after a regularization of the elliptic
spectrum through the extraction of a suitably chosen equivalent circulant
problem. The inverse of the system matrix is then obtained by leveraging the
Woodbury matrix identity, the low-rank representation of the extracted part of
the operator, and fast circulant algebra yielding a scheme with a favorable
complexity and suitable for the solution of multiple right-hand sides.
Theoretical considerations are accompanied by numerical results both of which
are confirming and showing the practical relevance of the newly developed
scheme.


title:
Local optimisation of Nystr\"om samples through stochastic gradient
  descent

abstract:
  We study a relaxed version of the column-sampling problem for the Nystr\"om
approximation of kernel matrices, where approximations are defined from
multisets of landmark points in the ambient space; such multisets are referred
to as Nystr\"om samples. We consider an unweighted variation of the radial
squared-kernel discrepancy (SKD) criterion as a surrogate for the classical
criteria used to assess the Nystr\"om approximation accuracy; in this setting,
we discuss how Nystr\"om samples can be efficiently optimised through
stochastic gradient descent. We perform numerical experiments which demonstrate
that the local minimisation of the radial SKD yields Nystr\"om samples with
improved Nystr\"om approximation accuracy.


title:
Continuous-Time Audiovisual Fusion with Recurrence vs. Attention for
  In-The-Wild Affect Recognition

abstract:
  In this paper, we present our submission to 3rd Affective Behavior Analysis
in-the-wild (ABAW) challenge. Learningcomplex interactions among multimodal
sequences is critical to recognise dimensional affect from in-the-wild
audiovisual data. Recurrence and attention are the two widely used sequence
modelling mechanisms in the literature. To clearly understand the performance
differences between recurrent and attention models in audiovisual affect
recognition, we present a comprehensive evaluation of fusion models based on
LSTM-RNNs, self-attention and cross-modal attention, trained for valence and
arousal estimation. Particularly, we study the impact of some key design
choices: the modelling complexity of CNN backbones that provide features to the
the temporal models, with and without end-to-end learning. We trained the
audiovisual affect recognition models on in-the-wild ABAW corpus by
systematically tuning the hyper-parameters involved in the network architecture
design and training optimisation. Our extensive evaluation of the audiovisual
fusion models shows that LSTM-RNNs can outperform the attention models when
coupled with low-complex CNN backbones and trained in an end-to-end fashion,
implying that attention models may not necessarily be the optimal choice for
continuous-time multimodal emotion recognition.


title: Preheating in Palatini Higgs inflation on the lattice

abstract:
  We study preheating following Higgs inflation in the Palatini formulation of
gravity. We numerically evolve perturbations of the radial mode of the Higgs
field and that of three scalars modeling the gauge bosons. We compare the two
non-perturbative mechanisms of growth of excitations -- parametric resonance
and tachyonic instability -- and confirm that the latter plays the dominant
role. Our results provide further evidence that preheating in Palatini Higgs
inflation happens within a single oscillation of the Higgs field about the
bottom of its potential, consistent with the approximation of an instantaneous
preheating.


title:
Bipolar Ephemeral Active Regions, Magnetic Flux Cancellation, and Solar
  Magnetic Explosions

abstract:
  We examine the cradle-to-grave magnetic evolution of 10 bipolar ephemeral
active regions (BEARs) in solar coronal holes, especially aspects of the
magnetic evolution leading to each of 43 obvious microflare events. The data
are from Solar Dynamics Observatory: 211 A coronal EUV images and line-of-sight
photospheric magnetograms. We find evidence that (1) each microflare event is a
magnetic explosion that results in a miniature flare arcade astride the
polarity inversion line (PIL) of the explosive lobe of the BEARs anemone
magnetic field; (2) relative to the BEAR's emerged flux-rope omega loop, the
anemone's explosive lobe can be an inside lobe, an outside lobe, or an inside &
outside lobe; (3) 5 events are confined explosions, 20 events are
mostly-confined explosions, and 18 events are blowout explosions, which are
miniatures of the magnetic explosions that make coronal mass ejections (CMEs);
(4) contrary to the expectation of Moore et al (2010), none of the 18 blowout
events explode from inside the BEARs omega loop during the omega loops
emergence; (5) before and during each of the 43 microflare events there is
magnetic flux cancellation at the PIL of the anemone's explosive lobe. From
finding evident flux cancellation at the underlying PIL before and during all
43 microflare events - together with BEARs evidently being miniatures of all
larger solar bipolar active regions - we expect that in essentially the same
way, flux cancellation in sunspot active regions prepares and triggers the
magnetic explosions for many major flares and CMEs.


title:
Improving the accuracy of estimators for the two-point correlation
  function

abstract:
  We show how to increase the accuracy of estimates of the two-point
correlation function without sacrificing efficiency. We quantify the error of
the pair-counts and of the Landy-Szalay estimator by comparing with exact
reference values. The standard method, using random point sets, is compared to
geometrically motivated estimators and estimators using quasi Monte-Carlo
integration. In the standard method the error scales proportional to
$1/\sqrt{N_r}$, with $N_r$ the number of random points. In our improved methods
the error is scaling almost proportional to $1/N_q$, where $N_q$ is the number
of points from a low discrepancy sequence. In an example we achieve a speedup
by a factor of $10^4$ over the standard method, still keeping the same level of
accuracy. We also discuss how to apply these improved estimators to
incompletely sampled galaxy catalogues.


title: Data-driven fluid mechanics of wind farms: A review

abstract:
  With the growing number of wind farms over the last decades and the
availability of large datasets, research in wind-farm flow modeling - one of
the key components in optimizing the design and operation of wind farms - is
shifting towards data-driven techniques. However, given that most current
data-driven algorithms have been developed for canonical problems, the enormous
complexity of fluid flows in real wind farms poses unique challenges for
data-driven flow modeling. These include the high-dimensional multiscale nature
of turbulence at high Reynolds numbers, geophysical and atmospheric effects,
wake-flow development, and incorporating wind-turbine characteristics and
wind-farm layouts, among others. In addition, data-driven wind-farm flow models
should ideally be interpretable and have some degree of generalizability. The
former is important to avoid a lack of trust in the models with end-users,
while the most popular strategy for the latter is to incorporate known physics
into the models. This article reviews a collection of recent studies on
wind-farm flow modeling covering both purely data-driven and physics-guided
approaches. We provide a thorough analysis of their modeling approach,
objective, and methodology, and specifically focus on the data utilized in the
reviewed works.


title: Kepler-1708 b-i is likely undetectable with HST

abstract:
  The exomoon candidate Kepler-1708 b-i was recently reported using two
transits of Kepler data. Supported by a 1% false-positive probability, the
candidate is promising but requires follow-up observations to confirm/reject
its validity. In this short paper, we calculate the detectability of the
exomoon candidate's transit in the next window (March 2023) using the WFC3
instrument aboard the Hubble Space Telescope (HST). Using realistic noise
estimates, accounting for the visit-long trends, and propagating the model
posteriors derived using the Kepler data, we perform 50 injection-recovery
trials with Bayesian model selection. Defining a detection criterion that the
moon transit must be detected to a Bayes factor exceeding 10 using HST alone,
only 18 of the injections were recovered, yielding a true-positive probability
(TPP) of $(36\pm7)$%. Despite HST's superior aperture to Kepler, both
instrumental systematics and the compactness of the candidate exomoon's orbit
typically obfuscate a strong detection. Although the noise properties of the
James Webb Space Telescope (JWST) have not yet been characterized in flight, we
estimate the signal would be easily recovered using NIRSpec operating in its
Bright Object Time Series mode.


title: Searching for fingerspelled content in American Sign Language

abstract:
  Natural language processing for sign language video - including tasks like
recognition, translation, and search - is crucial for making artificial
intelligence technologies accessible to deaf individuals, and is gaining
research interest in recent years. In this paper, we address the problem of
searching for fingerspelled key-words or key phrases in raw sign language
videos. This is an important task since significant content in sign language is
often conveyed via fingerspelling, and to our knowledge the task has not been
studied before. We propose an end-to-end model for this task, FSS-Net, that
jointly detects fingerspelling and matches it to a text sequence. Our
experiments, done on a large public dataset of ASL fingerspelling in the wild,
show the importance of fingerspelling detection as a component of a search and
retrieval model. Our model significantly outperforms baseline methods adapted
from prior work on related tasks


title:
Coordination and Collaboration: How do Volunteer Moderators Work as a
  Team in Live Streaming Communities?

abstract:
  Volunteer moderators (mods) play significant roles in developing moderation
standards and dealing with harmful content in their micro-communities. However,
little work explores how volunteer mods work as a team. In line with prior work
about understanding volunteer moderation, we interview 40 volunteer mods on
Twitch - a leading live streaming platform. We identify how mods collaborate on
tasks (off-streaming coordination and preparation, in-stream real-time
collaboration, and relationship building both off-stream and in-stream to
reinforce collaboration) and how mods contribute to moderation standards
(collaboratively working on the community rulebook and individually shaping
community norms). We uncover how volunteer mods work as an effective team. We
also discuss how the affordances of multi-modal communication and informality
of volunteer moderation contribute to task collaboration, standards
development, and mod's roles and responsibilities.


title:
Methods for Large-scale Single Mediator Hypothesis Testing: Possible
  Choices and Comparisons

abstract:
  Mediation hypothesis testing for a large number of mediators is challenging
due to the composite structure of the null hypothesis, H0:alpha*beta=0 (alpha:
effect of the exposure on the mediator after adjusting for confounders; beta:
effect of the mediator on the outcome after adjusting for exposure and
confounders). In this paper, we reviewed three classes of methods for multiple
mediation hypothesis testing. In addition to these existing methods, we
developed the Sobel-comp method, which uses a corrected mixture reference
distribution for Sobel's test statistic. We performed extensive simulation
studies to compare all six methods in terms of the false positive rates under
the null hypothesis and the true positive rates under the alternative
hypothesis. We found that the class of methods which uses a mixture reference
distribution could best maintain the false positive rates at the nominal level
under the null hypothesis and had the greatest true positive rates under the
alternative hypothesis. We applied all methods to study the mediation mechanism
of DNA methylation sites in the pathway from adult socioeconomic status to
glycated hemoglobin level using data from the Multi-Ethnic Study of
Atherosclerosis (MESA). We also provide guidelines for choosing the optimal
mediation hypothesis testing method in practice.


title: Learning Spatiotemporal Chaos Using Next-Generation Reservoir Computing

abstract:
  Forecasting the behavior of high-dimensional dynamical systems using machine
learning (ML) requires efficient methods to learn the underlying physical
model. We demonstrate spatiotemporal chaos prediction of a heuristic
atmospheric weather model using an ML architecture that, when combined with a
next-generation reservoir computer, displays state-of-the-art performance with
a training time $10^3-10^4$ times faster and training data set $\sim 10^2$
times smaller than other ML algorithms. We also take advantage of the
translational symmetry of the model to further reduce the computational cost
and training data, each by a factor of $\sim$10.


title: Detectable universes inside regular black holes

abstract:
  While spacetime in the vicinity outside astrophysical black holes is believed
to be well understood, the event horizon and the interior remain elusive. Here,
we discover a degenerate infinite spectrum of novel general relativity
solutions with the same mass-energy and entropy that describe a dark energy
universe inside an astrophysical black hole. This regular cosmological black
hole is stabilized by a finite tangential pressure applied on the dual
cosmological-black hole event horizon, localized up to a quantum indeterminacy.
We recover the Bekenstein-Hawking entropy formula from the classical fluid
entropy, calculated at a Tolman temperature equal to the cosmological horizon
temperature. We further calculate its gravitational quasi-normal modes. We find
that cosmological black holes are detectable by gravitational-wave experiments
operating within the $\mu{\rm Hz}-{\rm Hz}$ range, like LISA
space-interferometer.


title:
RayTran: 3D pose estimation and shape reconstruction of multiple objects
  from videos with ray-traced transformers

abstract:
  We propose a transformer-based neural network architecture for multi-object
3D reconstruction from RGB videos. It relies on two alternative ways to
represent its knowledge: as a global 3D grid of features and an array of
view-specific 2D grids. We progressively exchange information between the two
with a dedicated bidirectional attention mechanism. We exploit knowledge about
the image formation process to significantly sparsify the attention weight
matrix, making our architecture feasible on current hardware, both in terms of
memory and computation. We attach a DETR-style head on top of the 3D feature
grid in order to detect the objects in the scene and to predict their 3D pose
and 3D shape. Compared to previous methods, our architecture is single stage,
end-to-end trainable, and it can reason holistically about a scene from
multiple video frames without needing a brittle tracking step. We evaluate our
method on the challenging Scan2CAD dataset, where we outperform (1) recent
state-of-the-art methods for 3D object pose estimation from RGB videos; and (2)
a strong alternative method combining Multi-view Stereo with RGB-D CAD
alignment. We plan to release our source code.


title: The catastrophic fate of Schwarzschild black holes in a thermal bath

abstract:
  Using the Schwarzschild metric as a rudimentary toy-model, we pedagogically
revisit the curious prediction that the mass of a classical black hole in a
constant temperature thermal bath diverges in a finite amount of time. We study
in details how this instability behaves if the temperature of the bath is
allowed to vary with time and conclude that whatever the background behavior
(but for a zero-measure subspace of the initial conditions), the black hole
mass either diverges or vanishes in a finite time if the Hawking radiation is
taken into account. The competition between both effects is subtle and not
entirely governed by the hierarchy of the relevant temperatures. This
instability is also shown to be reached before the background singularity in a
contracting universe, which has implications for bouncing models. The results
are generalized to spaces with extra-dimensions and the main conclusions are
shown to remain true. The limitations of the model are reviewed, both from the
point of view of the dynamical black hole horizon and from the point of view of
the background space expansion. Comparisons with other approaches are suggested
and possible developments are underlined.


title:
Discovery of NES, an extended tidal structure in the North-East of the
  Large Magellanic Cloud

abstract:
  We report on the discovery of a new diffuse stellar sub-structure protruding
for 5 degrees from the North-Eastern rim of the LMC disc. The structure, that
we dub North-East Structure (NES), was identified by applying a Gaussian
Mixture Model to a sample of strictly selected candidate members of the
Magellanic System, extracted from the Gaia EDR3 catalogue. The NES fills the
gap between the outer LMC disk and other known structures in the same region of
the LMC, namely the Northern tidal arm (NTA) and the Eastern sub-structures
(ES). Particularly noteworthy is that the NES is placed in a region where
N-body simulations foresee a bending of the LMC disc due to tidal stresses
induced by the MW. The velocity field in the plane of the sky indicates that
the complex of tidal structures in the North-Eastern part of the LMC, including
NES, is subject to coherent radial motions. Additional data, as well as
extensive dynamical modeling, is required to shed light on the origin of NES as
well as on the relationships with the surrounding substructures.


title:
Mix and Match: Learning-free Controllable Text Generation using Energy
  Language Models

abstract:
  Recent work on controlled text generation has either required attribute-based
fine-tuning of the base language model (LM), or has restricted the
parameterization of the attribute discriminator to be compatible with the base
autoregressive LM. In this work, we propose Mix and Match LM, a global
score-based alternative for controllable text generation that combines
arbitrary pre-trained black-box models for achieving the desired attributes in
the generated text without involving any fine-tuning or structural assumptions
about the black-box models. We interpret the task of controllable generation as
drawing samples from an energy-based model whose energy values are a linear
combination of scores from black-box models that are separately responsible for
fluency, the control attribute, and faithfulness to any conditioning context.
We use a Metropolis-Hastings sampling scheme to sample from this energy-based
model using bidirectional context and global attribute features. We validate
the effectiveness of our approach on various controlled generation and
style-based text revision tasks by outperforming recently proposed methods that
involve extra training, fine-tuning, or restrictive assumptions over the form
of models.


title:
Visualizing quantum mechanics in an interactive simulation -- Virtual
  Lab by Quantum Flytrap

abstract:
  Virtual Lab by Quantum Flytrap explores novel ways to represent quantum
phenomena in an interactive and intuitive way. It is a no-code online
laboratory with a real-time simulation of an optical table, supporting up to
three entangled photons. Users can place typical optical elements (such as beam
splitters, polarizers, Faraday rotators, and detectors) with a drag-and-drop
graphical interface. Virtual Lab operates in two modes. The sandbox mode allows
users to compose arbitrary setups. Quantum Game serves as an introduction to
Virtual Lab features, approachable for users with no prior exposure to quantum
mechanics.
  We introduce novel ways of visualizing entangled quantum states and
displaying entanglement measures, including interactive visualizations of the
ket notation and a heatmap-like visualization of quantum operators. These
quantum visualizations can be applied to any discrete quantum system, including
quantum circuits with qubits and spin chains. These tools are available as
open-source TypeScript packages - Quantum Tensor and BraKetVue. Virtual Lab
makes it possible to explore the nature of quantum physics (state evolution,
entanglement, and measurement), to simulate quantum computing (e.g. the
Deutsch-Jozsa algorithm), to use quantum cryptography (e.g. the Ekert
protocol), to explore counterintuitive quantum phenomena (e.g. quantum
teleportation & the Bell inequality violation), and to recreate historical
experiments (e.g. the Michelson-Morley interferometer).
  Virtual Lab is available at: https://lab.quantumflytrap.com.


title: Multi-modal Multi-label Facial Action Unit Detection with Transformer

abstract:
  Facial Action Coding System is an important approach of facial expression
analysis.This paper describes our submission to the third Affective Behavior
Analysis (ABAW) 2022 competition. We proposed a transfomer based model to
detect facial action unit (FAU) in video. To be specific, we firstly trained a
multi-modal model to extract both audio and visual feature. After that, we
proposed a action units correlation module to learn relationships between each
action unit labels and refine action unit detection result. Experimental
results on validation dataset shows that our method achieves better performance
than baseline model, which verifies that the effectiveness of proposed network.


title: Democratizing LHC Data Analysis with ADL/CutLang

abstract:
  Data analysis at the LHC has a very steep learning curve, which erects a
formidable barrier between data and anyone who wishes to analyze data, either
to study an idea or to simply understand how data analysis is performed. To
make analysis more accessible, we designed the so-called Analysis Description
Language (ADL), a domain specific language capable of describing the contents
of an LHC analysis in a standard and unambiguous way, independent of any
computing frameworks. ADL has an English-like highly human-readable syntax and
directly employs concepts relevant to HEP. Therefore it eliminates the need to
learn complex analysis frameworks written based on general purpose languages
such as C++ or Python, and shifts the focus directly to physics. Analyses
written in ADL can be run on data using a runtime interpreter called CutLang,
without the necessity of programming. ADL and CutLang are designed for use by
anyone with an interest in, and/or knowledge of LHC physics, ranging from
experimentalists and phenomenologists to non-professional enthusiasts.
ADL/CutLang are originally designed for research, but are also equally intended
for education and public use. This approach has already been employed to train
undergraduate students with no programming experience in LHC analysis in two
dedicated schools in Turkey and Vietnam, and is being adapted for use with LHC
Open Data. Moreover, work is in progress towards piloting an educational module
in particle physics data analysis for high school students and teachers. Here,
we introduce ADL and CutLang and present the educational activities based on
these practical tools.


title: Sparse bounds for the bilinear spherical maximal function

abstract:
  We derive sparse bounds for the bilinear spherical maximal function in any
dimension $d\geq 1$. When $d\geq 2$, this immediately recovers the sharp
$L^p\times L^q\to L^r$ bound of the operator and implies quantitative weighted
norm inequalities with respect to bilinear Muckenhoupt weights, which seems to
be the first of their kind for the operator. The key innovation is a group of
newly developed continuity $L^p$ improving estimates for the single scale
bilinear spherical averaging operator.