title:
RFNet-4D: Joint Object Reconstruction and Flow Estimation from 4D Point
  Clouds

abstract:
  Object reconstruction from 3D point clouds has achieved impressive progress
in the computer vision and computer graphics research field. However,
reconstruction from time-varying point clouds (a.k.a. 4D point clouds) is
generally overlooked. In this paper, we propose a new network architecture,
namely RFNet-4D, that jointly reconstructs objects and their motion flows from
4D point clouds. The key insight is that simultaneously performing both tasks
via learning spatial and temporal features from a sequence of point clouds can
leverage individual tasks and lead to improved overall performance. The
proposed network can be trained using both supervised and unsupervised
learning. To prove this ability, we design a temporal vector field learning
module using an unsupervised learning approach for flow estimation, leveraged
by supervised learning of spatial structures for object reconstruction.
Extensive experiments and analyses on benchmark dataset validated the
effectiveness and efficiency of our method. As shown in experimental results,
our method achieves state-of-the-art performance on both flow estimation and
object reconstruction while performing much faster than existing methods in
both training and inference.


title: Ferromagnetism and doublon localization in a Wannier-Hubbard chain

abstract:
  We derive a "Wannier-Hubbard" model consisting of an array of overlapping
atomic orbitals interacting via a local Coulomb interaction. Transforming to an
orthogonal Wannier basis set, the resulting Hamiltonian displays long range
hopping and interactions, with new terms such as correlated hopping and
ferromagnetic direct exchange, among others. We numerically study the
one-dimensional version of the model at half-filling using the density matrix
renormalization group (DMRG) method, unveiling a rich phase diagram as a
function of the interaction $U$ and the overlap $s$ with metallic, and
ferromagnetic phases, separated by a ferrimagnetic region. Our results indicate
a path toward understanding new emergent phases under pressure and beyond
standard model Hamiltonians.


title: Two-Stage Robust Optimization with Decision Dependent Uncertainty

abstract:
  The type of decision dependent uncertainties (DDUs) imposes a great challenge
in decision making, while existing methodologies are not sufficient to support
many real practices. In this paper, we present a systematic study to handle
this challenge in two-stage robust optimization~(RO). Our main contributions
include three sophisticated variants of column-and-constraint generation method
to exactly compute DDU-based two-stage RO. By a novel application of core
concepts of linear programming, we provide rigorous analyses on their
computational behaviors. Interestingly, in terms of the iteration complexity of
those algorithms, DDU-based two-stage RO is not more demanding than its
decision independent uncertainty (DIU) based counterpart. It is worth
highlighting a counterintuitive discovery that converting a DIU set into a DDU
set by making use of "deep knowledge" and then computing the resulting
DDU-based formulation may lead to a significant improvement. Indeed, as shown
in this paper, in addition to capturing the actual dependence existing in the
real world, DDU is a powerful and flexible tool to represent and leverage
analytical properties or simply domain expertise to achieve a strong solution
capacity. So, we believe it will open a new direction to solve large-scale DIU-
or DDU-based RO. Other important results include basic structural properties
for two-stage RO, an approximation scheme to deal with mixed integer recourse,
and a couple of enhancement techniques for the developed algorithms, as well as
an organized numerical study to help us appreciate all algorithms and
enhancement techniques' computational performances.


title: Optimal Control of ensembles of dynamical systems

abstract:
  In this paper we consider the problem of the optimal control of an ensemble
of affine-control systems. After proving the well posedness of the minimization
problem in exam, we establish a $\Gamma$-convergence result that allows us to
substitute the original (and usually infinite) ensemble with a sequence of
finite increasing-in-size sub-ensembles. The solutions of the optimal control
problems involving these sub-ensembles provide approximations in the
$L^2$-strong topology of the minimizers of the original problem. Moreover, in
the case of finite sub-ensembles, we can address the minimization of the
related cost by means of numerical schemes. In particular, we propose an
algorithm that consists in a subspace projection of the gradient field induced
on the space of admissible controls by the approximating cost functional. In
addition, we consider an iterative method based on the Pontryagin Maximum
Principle. Finally, we test the algorithms on an ensemble of linear systems in
$\mathbb{R}^2$.


title: Tailored XZZX codes for biased noise

abstract:
  Quantum error correction (QEC) for generic errors is challenging due to the
demanding threshold and resource requirements. Interestingly, when physical
noise is biased, we can tailor our QEC schemes to the noise to improve
performance. Here we study a family of codes having XZZX-type stabilizer
generators, including a set of cyclic codes generalized from the five-qubit
code and a set of topological codes that we call generalized toric codes
(GTCs). We show that these XZZX codes are highly qubit efficient if tailored to
biased noise. To characterize the code performance, we use the notion of
effective distance, which generalizes code distance to the case of biased noise
and constitutes a proxy for the logical failure rate. We find that the XZZX
codes can achieve a favorable resource scaling by this metric under biased
noise. We also show that the XZZX codes have remarkably high thresholds that
reach what is achievable by random codes, and furthermore they can be
efficiently decoded using matching decoders. Finally, by adding only one flag
qubit, the XZZX codes can realize fault-tolerant QEC while preserving their
large effective distance. In combination, our results show that tailored XZZX
codes give a resource-efficient scheme for fault-tolerant QEC against biased
noise.


title:
Lossless Speedup of Autoregressive Translation with Generalized
  Aggressive Decoding

abstract:
  In this paper, we propose Generalized Aggressive Decoding (GAD) -- a novel
decoding paradigm for speeding up autoregressive translation without quality
loss, through the collaboration of autoregressive and non-autoregressive
translation (NAT) of the Transformer. At each decoding iteration, GAD
aggressively decodes a number of tokens in parallel as a draft with NAT and
then verifies them in the autoregressive manner, where only the tokens that
pass the verification are kept as decoded tokens. GAD can achieve the same
performance as autoregressive translation but much more efficiently because
both NAT drafting and autoregressive verification are fast due to parallel
computing. We conduct experiments in the WMT14 English-German translation task
and confirm that the vanilla GAD yields exactly the same results as greedy
decoding with an around 3x speedup, and that its variant (GAD++) with an
advanced verification strategy not only outperforms the greedy translation and
even achieves the comparable translation quality with the beam search result,
but also further improves the decoding speed, resulting in an around 5x speedup
over autoregressive translation. Our models and codes are available at
https://github.com/hemingkx/Generalized-Aggressive-Decoding.


title: Distributed quantum error correction for chip-level catastrophic errors

abstract:
  Quantum error correction holds the key to scaling up quantum computers.
Cosmic ray events severely impact the operation of a quantum computer by
causing chip-level catastrophic errors, essentially erasing the information
encoded in a chip. Here, we present a distributed error correction scheme to
combat the devastating effect of such events by introducing an additional layer
of quantum erasure error correcting code across separate chips. We show that
our scheme is fault tolerant against chip-level catastrophic errors and discuss
its experimental implementation using superconducting qubits with microwave
links. Our analysis shows that in state-of-the-art experiments, it is possible
to suppress the rate of these errors from 1 per 10 seconds to less than 1 per
month.


title:
Semantics between customers and providers: The relation between product
  descriptions, reviews, and customer satisfaction in E-commerce

abstract:
  In social commerce, users dialogue with each other on the topics related to
the providers' products. However, the language customers use may vary from the
language vendors use on their e-commerce websites and product descriptions.
This situation can lead to possible misunderstandings in the social dialogue
between customers, and incidental costs in the dialogue between customers and
vendors. One possible reason for this difference is that words used by
customers may have different meanings compared to those used by product
description writers. We present a novel approach to measure this potential
lexical-semantic gap for various e-commerce domains using an
information-theoretical approach based on a large corpus of user reviews and
product descriptions. Additionally, we use neural word embeddings to identify
words with the highest semantic drift between reviews and descriptions as a
tool to construct a ground truth for the task. We found that low levels of
lexical-semantic gap are related to better customer satisfaction. Besides, this
work is a step towards a better understanding of the effect of the use of
language in e-commerce and social commerce. Potential applications of this
technology could lead to better communication between customers and
improvements in customer satisfaction, services, and revenue. Furthermore, this
study opens up perspectives for applications in other domains with
relationships beyond client-vendor, such as citizen-government and
patient-healthcare system.


title: Foveation-based Deep Video Compression without Motion Search

abstract:
  The requirements of much larger file sizes, different storage formats, and
immersive viewing conditions of VR pose significant challenges to the goals of
acquiring, transmitting, compressing, and displaying high-quality VR content.
At the same time, the great potential of deep learning to advance progress on
the video compression problem has driven a significant research effort. Because
of the high bandwidth requirements of VR, there has also been significant
interest in the use of space-variant, foveated compression protocols. We have
integrated these techniques to create an end-to-end deep learning video
compression framework. A feature of our new compression model is that it
dispenses with the need for expensive search-based motion prediction
computations. This is accomplished by exploiting statistical regularities
inherent in video motion expressed by displaced frame differences. Foveation
protocols are desirable since only a small portion of a video viewed in VR may
be visible as a user gazes in any given direction. Moreover, even within a
current field of view (FOV), the resolution of retinal neurons rapidly
decreases with distance (eccentricity) from the projected point of gaze. In our
learning based approach, we implement foveation by introducing a Foveation
Generator Unit (FGU) that generates foveation masks which direct the allocation
of bits, significantly increasing compression efficiency while making it
possible to retain an impression of little to no additional visual loss given
an appropriate viewing geometry. Our experiment results reveal that our new
compression model, which we call the Foveated MOtionless VIdeo Codec (Foveated
MOVI-Codec), is able to efficiently compress videos without computing motion,
while outperforming foveated version of both H.264 and H.265 on the widely used
UVG dataset and on the HEVC Standard Class B Test Sequences.


title:
Analytical insight into dark matter subhalo boost factors for
  Sommerfeld-enhanced $s$- and $p$-wave $\gamma$-ray signals

abstract:
  As searches for thermal and self-annihilating dark matter (DM) intensify, it
becomes crucial to include as many relevant physical processes and ingredients
as possible to refine signal predictions, in particular those which directly
relate to the intimate properties of DM. We investigate the combined impact of
DM subhalos and the (velocity-dependent) Sommerfeld enhancement of the
annihilation cross section, in both the $s$- and $p$-wave cases. Both features
are expected to play an important role in searches for thermal DM particle
candidates with masses around or beyond TeV, or in scenarios with a light dark
sector. We provide a detailed analytical description of the phenomena at play,
and show how they scale with the subhalo masses and the main Sommerfeld
parameters. We derive approximate analytical expressions for the overall boost
factors resulting from these combined effects, from which the intricate
phenomenology can be better understood, and which strongly increase gamma-ray
signal predictions for typical targets of different masses (from dwarf galaxies
to galaxy clusters). DM subhalos lead to an increase of the Sommerfeld effect
by several orders of magnitude (for both the $s$- and $p$-wave cases),
especially on resonances, which makes them critical to get sensible
predictions.

categories: astro-ph.CO astro-ph.GA astro-ph.HE hep-ph


title:
On the impact of dimensionally-consistent and physics-based inner
  products for POD-Galerkin and least-squares model reduction of compressible
  flows

abstract:
  Model reduction of the compressible Euler equations based on proper
orthogonal decomposition (POD) and Galerkin orthogonality or least-squares
residual minimization requires the selection of inner product spaces in which
to perform projections and measure norms. The most popular choice is the
vector-valued L2({\Omega}) inner product space. This choice, however, yields
dimensionally-inconsistent reduced-order model (ROM) formulations which often
lack robustness. In this work, we try to address this weakness by studying a
set of dimensionally-consistent inner products with application to the
compressible Euler equations. First, we demonstrate that non-dimensional inner
products have a positive impact on both POD and Galerkin/least-squares ROMs.
Second, we further demonstrate that physics-based inner products based on
entropy principles result in drastically more accurate and robust ROM
formulations than those based on non-dimensional L2({\Omega}) inner products.As
test cases, we consider the following well-known problems: the one-dimensional
Sod shock tube, the two-dimensional Kelvin-Helmholtz instability and
two-dimensional homogeneous isotropic turbulence.


title:
Numerical Study of Cosserat Fluid-Structure Interaction in a Monolithic
  Eulerian Framework

abstract:
  We propose a monolithic Eulerian variational formulation in non-classical
sense of continuum description for the analysis of micro-viscosity parameters
at micro-structural level. In this respect, Cosesrat fluid-structure
interaction CFSI phenomena is taken into account by considering
micro-rotational degrees of freedom of the particles. The governing equations
and variational formulation for CFSI problem are presented. The space and time
domains are discretized by the finite element method and semi-implicit scheme.
The model is implemented and evaluated using FreeFem++. The present model is
analyzed by computing a well known benchmark problem FLUSTRUK-FSI-3*, where the
flow around a flag attached with cylinder is considered for numerical test. The
obtained results suggest that the amplitude of oscillations and
micro-rotational viscosity varies inversely and micro-viscosity parameter
exhibit a significant effect on micro-rotation field as compare to velocity
field.


title:
S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order
  Models

abstract:
  While projection-based reduced order models can reduce the dimension of full
order solutions, the resulting reduced models may still contain terms that
scale with the full order dimension. Hyper-reduction techniques are
sampling-based methods that further reduce this computational complexity by
approximating such terms with a much smaller dimension. The goal of this work
is to introduce a points selection algorithm developed by Shin and Xiu [SIAM J.
Sci. Comput., 38 (2016), pp. A385--A411], as a hyper-reduction method. The
selection algorithm is originally proposed as a stochastic collocation method
for uncertainty quantification. Since the algorithm aims at maximizing a
quantity S that measures both the column orthogonality and the determinant, we
refer to the algorithm as S-OPT. Numerical examples are provided to demonstrate
the performance of S-OPT and to compare its performance with an over-sampled
Discrete Empirical Interpolation (DEIM) algorithm. We found that using the
S-OPT algorithm is shown to predict the full order solutions with higher
accuracy for a given number of indices.


title: Crowdsourcing Creative Work

abstract:
  This article-based doctoral thesis explores the stakeholder perspectives and
experiences of crowdsourced creative work on two of the leading crowdsourcing
platforms. The thesis has two parts. In the first part, we explore creative
work from the perspective of the crowd worker. In the second part, we explore
and study the requester's perspective in different contexts and several case
studies. The research is exploratory and we contribute empirical insights using
survey-based and artefact-based approaches common in the field of
Human-Computer Interaction (HCI). In the former approach, we explore the key
issues that may limit creative work on paid crowdsourcing platforms. In the
latter approach, we create computational artefacts to elicit authentic
experiences from both crowd workers and requesters of crowdsourced creative
work. The thesis contributes a classification of crowd workers into five
archetypal profiles, based on the crowd workers' demographics, disposition, and
preferences for creative work. We propose a three-part classification of
creative work on crowdsourcing platforms: creative tasks, creativity tests, and
creativity judgements (also referred to as creative feedback). The thesis
further investigates the emerging research topic of how requesters can be
supported in interpreting and evaluating complex creative work. Last, we
discuss the design implications for research and practice and contribute a
vision of creative work on future crowdsourcing platforms with the aim of
empowering crowd workers and fostering an ecosystem around tailored platforms
for creative microwork.
  Keywords: creative work, creativity, creativity support tools, crowdsourcing


title:
Comment on A novel two-dimensional boron carbon nitride (BCN) monolayer:
  A first-principles insight [J. Appl. Phys. 2021, 130, 114301]

abstract:
  Recently, Bafekry et al. [J. Appl. Phys. 130, 114301(2021)] reported their
density functional theory (DFT) results on the structural, phonon dispersion,
elastic constants and electronic properties of a BCN monolayer. The
aforementioned theoretical work however includes erroneous results and
discussions, as will be explained in the following.


title:
An Artificial Intelligence Browser Architecture (AIBA) For Our Kind and
  Others: A Voice Name System Speech implementation with two warrants, Wake
  Neutrality and Value Preservation of Personally Identifiable Information

abstract:
  Conversational commerce, first pioneered by Apple's Siri, is the first of may
applications based on always-on artificial intelligence systems that decide on
its own when to interact with the environment, potentially collecting 24x7
longitudinal training data that is often Personally Identifiable Information
(PII). A large body of scholarly papers, on the order of a million according to
a simple Google Scholar search, suggests that the treatment of many health
conditions, including COVID-19 and dementia, can be vastly improved by this
data if the dataset is large enough as it has happened in other domains (e.g.
GPT3). In contrast, current dominant systems are closed garden solutions
without wake neutrality and that can't fully exploit the PII data they have
because of IRB and Cohues-type constraints.
  We present a voice browser-and-server architecture that aims to address these
two limitations by offering wake neutrality and the possibility to handle PII
aiming to maximize its value. We have implemented this browser for the
collection of speech samples and have successfully demonstrated it can capture
over 200.000 samples of COVID-19 coughs. The architecture we propose is
designed so it can grow beyond our kind into other domains such as collecting
sound samples from vehicles, video images from nature, ingestible robotics,
multi-modal signals (EEG, EKG,...), or even interacting with other kinds such
as dogs and cats.


title:
Ab initio Approaches to High Entropy Alloys: A Comparison of CPA, SQS,
  and Supercell Methods

abstract:
  We present a comparative study of different modeling approaches to the
electronic properties of the
$\textrm{Hf}_{0.05}\textrm{Nb}_{0.05}\textrm{Ta}_{0.8}\textrm{Ti}_{0.05}\textrm{Zr}_{0.05}$
high entropy alloy. Common to our modeling is the methodology to compute the
one-particle Green's function in the framework of density functional theory. We
demonstrate that the special quasi-random structures modeling and the
supercell, i.e. the locally self-consistent multiple-scatering methods provide
very similar results for the ground state properties such as the spectral
function (density of states) and the equilibrium lattice parameter. To
reconcile the multiple-scattering single-site coherent potential approximation
with the real space supercell methods, we included the effect of screening of
the net charges of the alloy components. Based on the analysis of the total
energy and spectral functions computed within the density functional theory, we
found no signature for the long-range or local magnetic moments formation in
the
$\textrm{Hf}_{0.05}\textrm{Nb}_{0.05}\textrm{Ta}_{0.8}\textrm{Ti}_{0.05}\textrm{Zr}_{0.05}$
high entropy alloy, instead we find possible superconductivity below $\sim 9$K.


title:
Forensic Analysis and Localization of Multiply Compressed MP3 Audio
  Using Transformers

abstract:
  Audio signals are often stored and transmitted in compressed formats. Among
the many available audio compression schemes, MPEG-1 Audio Layer III (MP3) is
very popular and widely used. Since MP3 is lossy it leaves characteristic
traces in the compressed audio which can be used forensically to expose the
past history of an audio file. In this paper, we consider the scenario of audio
signal manipulation done by temporal splicing of compressed and uncompressed
audio signals. We propose a method to find the temporal location of the splices
based on transformer networks. Our method identifies which temporal portions of
a audio signal have undergone single or multiple compression at the temporal
frame level, which is the smallest temporal unit of MP3 compression. We tested
our method on a dataset of 486,743 MP3 audio clips. Our method achieved higher
performance and demonstrated robustness with respect to different MP3 data when
compared with existing methods.


title:
The Role of the Neutrino Form Factors in the Energy Loss Rates of Pair
  Annihilation Process

abstract:
  The stellar energy loss rates due to the production of the neutrino pair
process $e^+e^- \rightarrow (W, Z, \gamma) \rightarrow \nu_e \bar{\nu_e}$ are
calculated in the minimal extension of Standard Model with electromagnetic
properties of Dirac neutrinos which takes the account of the contribution of
the neutrino charge radius, anapole moment and dipole moments. We have shown
that the contribution of the electron neutrino's dipole moment is small
compared with that of the charge radius. The obtained results are also
considered with results obtained using the Standard Model.


title:
The Signal-to-Noise Ratio for Photon Counting After Photometric
  Corrections

abstract:
  Photon counting is a mode of processing astronomical observations of
low-signal targets that have been observed using an electron multiplying CCD
(EMCCD). In photon counting, the EMCCD amplifies the signal, and a thresholding
technique effectively selects for the signal photo-electrons while drastically
reducing relative noise sources.
  Photometric corrections have been developed which result in the extraction of
a more accurate signal of photo-electrons, and the Nancy Grace Roman Telescope
will utilize a theoretical expression for the signal-to-noise ratio (SNR) given
these corrections based on well-calibrated noise parameters to plan
observations taken by its coronagraph instrument. We derive here analytic
expressions for the SNR for the method of photon counting, before and after
these photometric corrections have been applied.


title: Generative Spoken Dialogue Language Modeling

abstract:
  We introduce dGSLM, the first "textless" model able to generate audio samples
of naturalistic spoken dialogues. It uses recent work on unsupervised spoken
unit discovery coupled with a dual-tower transformer architecture with
cross-attention trained on 2000 hours of two-channel raw conversational audio
(Fisher dataset) without any text or labels. It is able to generate speech,
laughter and other paralinguistic signals in the two channels simultaneously
and reproduces naturalistic turn taking. Generation samples can be found at:
https://speechbot.github.io/dgslm.


title:
Rational K-matrices for finite-dimensional representations of quantum
  affine algebras

abstract:
  Let $\mathfrak{g}$ be a complex simple Lie algebra. We prove that every
finite-dimensional representation of the (untwisted) quantum affine algebra
$U_qL\mathfrak{g}$ gives rise to a family of spectral K-matrices, namely
solutions of Cherednik's generalized reflection equation, which depends upon
the choice of a quantum affine symmetric pair $U_q\mathfrak{k}\subset
U_qL\mathfrak{g}$. Moreover, we prove that every irreducible representation
over $U_qL\mathfrak{g}$ remains generically irreducible under restriction to
$U_q\mathfrak{k}$. From the latter result, we deduce that every obtained
K-matrix can be normalized to a matrix-valued rational function in a
multiplicative parameter, known in the study of quantum integrability as a
trigonometric K-matrix. Finally, we show that our construction recovers many of
the known solutions of the standard reflection equation and gives rise to a
large class of new solutions.


title:
The CARMENES search for exoplanets around M dwarfs: Two Saturn-mass
  planets orbiting active stars

abstract:
  The CARMENES radial-velocity survey is currently searching for planets in a
sample of 387 M dwarfs. Here we report on two Saturn-mass planets orbiting TYC
2187-512-1 ($M_\star = 0.50 M_\odot$) and TZ Ari ($M_\star = 0.15 M_\odot$),
respectively. We obtained supplementary photometric time series, which we use
along with spectroscopic information to determine the rotation periods of the
two stars. In both cases, the radial velocities also show strong modulations at
the respective rotation period. We thus modeled the radial velocities as a
Keplerian orbit plus a Gaussian process representing the stellar variability.
TYC 2187-512-1 is found to harbor a planet with a minimum mass of 0.33 $M_{\rm
Jup}$ in a near-circular 692-day orbit. The companion of TZ Ari has a minimum
mass of 0.21 $M_{\rm Jup}$, orbital period of 771 d, and orbital eccentricity
of 0.46. We provide an overview of all known giant planets in the CARMENES
sample, from which we infer an occurrence rate of giant planets orbiting M
dwarfs with periods up to 2 years in the range between 2% to 6%. TZ Ari b is
only the second giant planet discovered orbiting a host with mass less than 0.3
$M_\odot$. These objects occupy an extreme location in the planet mass versus
host mass plane. It is difficult to explain their formation in core-accretion
scenarios, so they may possibly have been formed through a disk fragmentation
process.


title: Fast, Accurate and Memory-Efficient Partial Permutation Synchronization

abstract:
  Previous partial permutation synchronization (PPS) algorithms, which are
commonly used for multi-object matching, often involve computation-intensive
and memory-demanding matrix operations. These operations become intractable for
large scale structure-from-motion datasets. For pure permutation
synchronization, the recent Cycle-Edge Message Passing (CEMP) framework
suggests a memory-efficient and fast solution. Here we overcome the restriction
of CEMP to compact groups and propose an improved algorithm, CEMP-Partial, for
estimating the corruption levels of the observed partial permutations. It
allows us to subsequently implement a nonconvex weighted projected power method
without the need of spectral initialization. The resulting new PPS algorithm,
MatchFAME (Fast, Accurate and Memory-Efficient Matching), only involves sparse
matrix operations, and thus enjoys lower time and space complexities in
comparison to previous PPS algorithms. We prove that under adversarial
corruption, though without additive noise and with certain assumptions,
CEMP-Partial is able to exactly classify corrupted and clean partial
permutations. We demonstrate the state-of-the-art accuracy, speed and memory
efficiency of our method on both synthetic and real datasets.


title:
An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for
  Face Mask Detection

abstract:
  Coronavirus 2019 has brought severe challenges to social stability and public
health worldwide. One effective way of curbing the epidemic is to require
people to wear masks in public places and monitor mask-wearing states by
utilizing suitable automatic detectors. However, existing deep learning based
models struggle to simultaneously achieve the requirements of both high
precision and real-time performance. To solve this problem, we propose an
improved lightweight face mask detector based on YOLOv5, which can achieve an
excellent balance of precision and speed. Firstly, a novel backbone
ShuffleCANet that combines ShuffleNetV2 network with Coordinate Attention
mechanism is proposed as the backbone. Afterwards, an efficient path aggression
network BiFPN is applied as the feature fusion neck. Furthermore, the
localization loss is replaced with alpha-CIoU in model training phase to obtain
higher-quality anchors. Some valuable strategies such as data augmentation,
adaptive image scaling, and anchor cluster operation are also utilized.
Experimental results on AIZOO face mask dataset show the superiority of the
proposed model. Compared with the original YOLOv5, the proposed model increases
the inference speed by 28.3% while still improving the precision by 0.58%. It
achieves the best mean average precision of 95.2% compared with other seven
existing models, which is 4.4% higher than the baseline.


title: AdaMixer: A Fast-Converging Query-Based Object Detector

abstract:
  Traditional object detectors employ the dense paradigm of scanning over
locations and scales in an image. The recent query-based object detectors break
this convention by decoding image features with a set of learnable queries.
However, this paradigm still suffers from slow convergence, limited
performance, and design complexity of extra networks between backbone and
decoder. In this paper, we find that the key to these issues is the
adaptability of decoders for casting queries to varying objects. Accordingly,
we propose a fast-converging query-based detector, named AdaMixer, by improving
the adaptability of query-based decoding processes in two aspects. First, each
query adaptively samples features over space and scales based on estimated
offsets, which allows AdaMixer to efficiently attend to the coherent regions of
objects. Then, we dynamically decode these sampled features with an adaptive
MLP-Mixer under the guidance of each query. Thanks to these two critical
designs, AdaMixer enjoys architectural simplicity without requiring dense
attentional encoders or explicit pyramid networks. On the challenging MS COCO
benchmark, AdaMixer with ResNet-50 as the backbone, with 12 training epochs,
reaches up to 45.0 AP on the validation set along with 27.9 APs in detecting
small objects. With the longer training scheme, AdaMixer with ResNeXt-101-DCN
and Swin-S reaches 49.5 and 51.3 AP. Our work sheds light on a simple,
accurate, and fast converging architecture for query-based object detectors.
The code is made available at https://github.com/MCG-NJU/AdaMixer


title:
Hadronic vacuum polarization contribution to the muon $g-2$ in
  holographic QCD

abstract:
  We evaluate the leading-order hadronic vacuum polarization contribution to
the anomalous magnetic moment of the muon with two light flavors in minimal
hard-wall and soft-wall holographic QCD models, as well as in simple
generalizations thereof, and compare with the rather precise results available
from dispersive and lattice approaches. While holographic QCD cannot be
expected to shed light on the existing small discrepancies between the latter,
this comparison in turn provides useful information on the holographic models,
which have been used to evaluate hadronic light-by-light contributions where
errors in data-driven and lattice approaches are more sizable. In particular,
in the hard-wall model that has recently been used to implement the
Melnikov-Vainshtein short-distance constraint on hadronic light-by-light
contributions, a matching of the hadronic vacuum polarization to the
data-driven approach points to the same correction of parameters that has been
proposed recently in order to account for next-to-leading order effects.


title:
High dynamic range electro-optic dual-comb interrogation of
  optomechanical sensors

abstract:
  An interleaved, chirped electro-optic dual comb system is demonstrated for
rapid, high dynamic range measurements of cavity optomechanical sensors. This
approach allows for the cavity displacements to be interrogated at measurement
times as fast as 10 {\mu}s over ranges far larger than can be achieved with
alternative methods. While the performance of this novel readout approach is
evaluated with an optomechanical accelerometer, this method is applicable to a
wide range of applications including temperature, pressure, and humidity
sensing as well as acoustics and molecular spectroscopy.


title: Vector Bessel beams: general classification and scattering simulations

abstract:
  Apart from a lot of fundamental interest, vector Bessel beams are widely used
in optical manipulation, material processing, and imaging. However, the
existing description of such beams remains fragmentary, especially when their
scattering by small particles is considered. We propose a new general
classification of all existing vortex Bessel beam types in an isotropic medium
based on the superposition of transverse Hertz vector potentials. This
theoretical framework contains duality and coordinate rotations as elementary
matrix operations and naturally describes all relations between various beam
types. This leads to various bases for Bessel beams and uncovers the novel beam
type with circularly symmetric energy density. We also discuss quadratic
functionals of the fields (such as the energy density and Poynting vector) and
derive orthogonality relations between various beam types. Altogether, it
provides a comprehensive reference of all properties of Bessel beams that may
be relevant for applications. Next we generalize the formalism of the Mueller
scattering matrices to arbitrary Bessel beams accounting for their vorticity.
Finally, we implement these beams in the ADDA code - an open-source parallel
implementation of the discrete dipole approximation. This enables easy and
efficient simulation of Bessel-beams scattering by particles with arbitrary
shape and internal structure.


title: Super-exponential distinguishability of correlated quantum states

abstract:
  In the problem of asymptotic binary i.i.d. state discrimination, the optimal
asymptotics of the type I and the type II error probabilities is in general an
exponential decrease to zero as a function of the number of samples; the set of
achievable exponent pairs is characterized by the quantum Hoeffding bound
theorem. A super-exponential decrease for both types of error probabilities is
only possible in the trivial case when the two states are orthogonal, and hence
can be perfectly distinguished using only a single copy of the system.
  In this paper we show that a qualitatively different behaviour can occur when
there is correlation between the samples. Namely, we use gauge-invariant and
translation-invariant quasi-free states on the algebra of the canonical
anti-commutation relations to exhibit pairs of states on an infinite spin chain
with the properties that a) all finite-size restrictions of the states have
invertible density operators, and b) the type I and the type II error
probabilities both decrease to zero at least with the speed $e^{-nc\log n}$
with some positive constant $c$, i.e., with a super-exponential speed in the
sample size $n$.


title: Vakyansh: ASR Toolkit for Low Resource Indic languages

abstract:
  We present Vakyansh, an end to end toolkit for Speech Recognition in Indic
languages. India is home to almost 121 languages and around 125 crore speakers.
Yet most of the languages are low resource in terms of data and pretrained
models. Through Vakyansh, we introduce automatic data pipelines for data
creation, model training, model evaluation and deployment. We create 14,000
hours of speech data in 23 Indic languages and train wav2vec 2.0 based
pretrained models. These pretrained models are then finetuned to create state
of the art speech recognition models for 18 Indic languages which are followed
by language models and punctuation restoration models. We open source all these
resources with a mission that this will inspire the speech community to develop
speech first applications using our ASR models in Indic languages.


title: PromptDet: Expand Your Detector Vocabulary with Uncurated Images

abstract:
  The goal of this work is to establish a scalable pipeline for expanding an
object detector towards novel/unseen categories, using zero manual annotations.
To achieve that, we make the following four contributions: (i) in pursuit of
generalisation, we propose a two-stage open-vocabulary object detector that
categorises each box proposal by a classifier generated from the text encoder
of a pre-trained visual-language model; (ii) To pair the visual latent space
(from RPN box proposal) with that of the pre-trained text encoder, we propose
the idea of regional prompt learning to optimise a couple of learnable prompt
vectors, converting the textual embedding space to fit those visually
object-centric images; (iii) To scale up the learning procedure towards
detecting a wider spectrum of objects, we exploit the available online
resource, iteratively updating the prompts, and later self-training the
proposed detector with pseudo labels generated on a large corpus of noisy,
uncurated web images. The self-trained detector, termed as PromptDet,
significantly improves the detection performance on categories for which manual
annotations are unavailable or hard to obtain, e.g. rare categories. Finally,
(iv) to validate the necessity of our proposed components, we conduct extensive
experiments on the challenging LVIS and MS-COCO dataset, showing superior
performance over existing approaches with fewer additional training images and
zero manual annotations whatsoever. Project page with code:
https://fcjian.github.io/promptdet.


title: Exceptional sets for geodesic flows of noncompact manifolds

abstract:
  For a geodesic flow on a negatively curved Riemannian manifold $M$ and some
subset $A\subset T^1M$, we study the limit $A$-exceptional set, that is the set
of points whose $\omega$-limit do not intersect $A$. We show that if the
topological $\ast$-entropy of $A$ is smaller than the topological entropy of
the geodesic flow, then the limit $A$-exceptional set has full topological
entropy. Some consequences are stated for limit exceptional sets of invariant
compact subsets and proper submanifolds.


title: Fast Light-Weight Near-Field Photometric Stereo

abstract:
  We introduce the first end-to-end learning-based solution to near-field
Photometric Stereo (PS), where the light sources are close to the object of
interest. This setup is especially useful for reconstructing large immobile
objects. Our method is fast, producing a mesh from 52 512$\times$384 resolution
images in about 1 second on a commodity GPU, thus potentially unlocking several
AR/VR applications. Existing approaches rely on optimization coupled with a
far-field PS network operating on pixels or small patches. Using optimization
makes these approaches slow and memory intensive (requiring 17GB GPU and 27GB
of CPU memory) while using only pixels or patches makes them highly susceptible
to noise and calibration errors. To address these issues, we develop a
recursive multi-resolution scheme to estimate surface normal and depth maps of
the whole image at each step. The predicted depth map at each scale is then
used to estimate `per-pixel lighting' for the next scale. This design makes our
approach almost 45$\times$ faster and 2$^{\circ}$ more accurate (11.3$^{\circ}$
vs. 13.3$^{\circ}$ Mean Angular Error) than the state-of-the-art near-field PS
reconstruction technique, which uses iterative optimization.


title:
Designing a Transactive Electric Vehicle Agent with Customer's
  Participation Preference

abstract:
  The proliferation of electric vehicles (EVs) and their inherent flexibility
in charging timings make them an asset to improve grid performance. In contrast
to direct control by a utility or autonomous price-based charging, the
transactive control framework not only provides benefits to both grid and
customers but also ensures customer autonomy. In this work, we design a
transactive electric vehicle (TEV) agent that incorporates the EV owner's
willingness to trade-off between savings and amenity in form of a slider, where
the EV owner's amenity is characterized as vehicle readiness. Further, a
privacy-preserving bidding formulation is proposed that also represents the
customer's transactive preference. A transactive market mechanism is discussed
that integrates the TEV Agents into the local retail market and reconciles with
the current day-ahead and real-time market structure. It is demonstrated that
the proposed slider is able to provide a preferred trade-off between savings
and amenity to individual customers. At the same time, the market mechanism is
shown to successfully reduce both peak prices and peak demand. A comparative
investigation of V1G and V2G technologies with respect to the battery prices is
also discussed.


title: Unseen Classes at a Later Time? No Problem

abstract:
  Recent progress towards learning from limited supervision has encouraged
efforts towards designing models that can recognize novel classes at test time
(generalized zero-shot learning or GZSL). GZSL approaches assume knowledge of
all classes, with or without labeled data, beforehand. However, practical
scenarios demand models that are adaptable and can handle dynamic addition of
new seen and unseen classes on the fly (that is continual generalized zero-shot
learning or CGZSL). One solution is to sequentially retrain and reuse
conventional GZSL methods, however, such an approach suffers from catastrophic
forgetting leading to suboptimal generalization performance. A few recent
efforts towards tackling CGZSL have been limited by difference in settings,
practicality, data splits and protocols followed-inhibiting fair comparison and
a clear direction forward. Motivated from these observations, in this work, we
firstly consolidate the different CGZSL setting variants and propose a new
Online-CGZSL setting which is more practical and flexible. Secondly, we
introduce a unified feature-generative framework for CGZSL that leverages
bi-directional incremental alignment to dynamically adapt to addition of new
classes, with or without labeled data, that arrive over time in any of these
CGZSL settings. Our comprehensive experiments and analysis on five benchmark
datasets and comparison with baselines show that our approach consistently
outperforms existing methods, especially on the more practical Online setting.


title: Collaborative Transformers for Grounded Situation Recognition

abstract:
  Grounded situation recognition is the task of predicting the main activity,
entities playing certain roles within the activity, and bounding-box groundings
of the entities in the given image. To effectively deal with this challenging
task, we introduce a novel approach where the two processes for activity
classification and entity estimation are interactive and complementary. To
implement this idea, we propose Collaborative Glance-Gaze TransFormer
(CoFormer) that consists of two modules: Glance transformer for activity
classification and Gaze transformer for entity estimation. Glance transformer
predicts the main activity with the help of Gaze transformer that analyzes
entities and their relations, while Gaze transformer estimates the grounded
entities by focusing only on the entities relevant to the activity predicted by
Glance transformer. Our CoFormer achieves the state of the art in all
evaluation metrics on the SWiG dataset. Training code and model weights are
available at https://github.com/jhcho99/CoFormer.


title:
Non-volatile Electric Field Control of Thermal Magnons in the Absence of
  an Applied Magnetic Field

abstract:
  Spin transport through magnetic insulators has been demonstrated in a variety
of materials and is an emerging pathway for next-generation spin-based
computing. To modulate spin transport in these systems, one typically applies a
sufficiently strong magnetic field to allow for deterministic control of
magnetic order. Here, we make use of the well-known multiferroic
magnetoelectric, BiFeO3, to demonstrate non-volatile, hysteretic,
electric-field control of thermally excited magnon current in the absence of an
applied magnetic field. These findings are an important step toward
magnon-based devices, where electric-field-only control is highly desirable.


title:
Highly-Multiplexed Superconducting Detector Readout: Approachable
  High-Speed FPGA Design

abstract:
  This work presents the design and preliminary performance of a
highly-multiplexed superconducting detector readout. The readout system is
implemented on the Xilinx ZCU111 RFSoC Evaluation Board. The current design
uses 12% of the DSPs, 60% of the LUTs, 20% of the FFs, and 30% of the BRAM and
makes timing at 512 MHz. The system uses two integrated ADCs and DACs running
at 4.096 GSPS to read out 2,048 superconducting detectors. This work targets a
2x increase in the number of superconducting detectors processed per board with
80% less power than previous readout schemes. The open-source design leverages
modern FPGA productivity tools including Vivado High-Level Synthesis to create
all custom IP blocks, PYNQ to test and verify individual IP and develop Python
drivers, and Vivado ML Intelligent Design Runs to close timing. We emphasize
strategies for achieving timing closure without custom HDL which we expect to
be useful for superconducting device groups looking to utilize FPGAs in
high-performance applications without specialized knowledge in FPGA design.


title: CoordGAN: Self-Supervised Dense Correspondences Emerge from GANs

abstract:
  Recent advances show that Generative Adversarial Networks (GANs) can
synthesize images with smooth variations along semantically meaningful latent
directions, such as pose, expression, layout, etc. While this indicates that
GANs implicitly learn pixel-level correspondences across images, few studies
explored how to extract them explicitly. In this work, we introduce Coordinate
GAN (CoordGAN), a structure-texture disentangled GAN that learns a dense
correspondence map for each generated image. We represent the correspondence
maps of different images as warped coordinate frames transformed from a
canonical coordinate frame, i.e., the correspondence map, which describes the
structure (e.g., the shape of a face), is controlled via a transformation.
Hence, finding correspondences boils down to locating the same coordinate in
different correspondence maps. In CoordGAN, we sample a transformation to
represent the structure of a synthesized instance, while an independent texture
branch is responsible for rendering appearance details orthogonal to the
structure. Our approach can also extract dense correspondence maps for real
images by adding an encoder on top of the generator. We quantitatively
demonstrate the quality of the learned dense correspondences through
segmentation mask transfer on multiple datasets. We also show that the proposed
generator achieves better structure and texture disentanglement compared to
existing approaches. Project page: https://jitengmu.github.io/CoordGAN/


title: Locally s-arc-transitive graphs arising from product action

abstract:
  We study locally $s$-arc-transitive graphs arising from the quasiprimitive
product action (PA). We prove that, for any locally $(G,2)$-arc-transitive
graph with $G$ acting quasiprimitively with type PA on both $G$-orbits of
vertices, the group $G$ does not act primitively on either orbit. Moreover, we
construct the first examples of locally $s$-arc-transitive graphs of PA type
that are not standard double covers of $s$-arc-transitive graphs of PA type,
answering the existence question for these graphs.


title: Shifted Witten classes and topological recursion

abstract:
  The Witten $r$-spin class defines a non-semisimple cohomological field
theory. Pandharipande, Pixton and Zvonkine studied two special shifts of the
Witten class along two semisimple directions of the associated
Dubrovin--Frobenius manifold using the Givental--Teleman reconstruction
theorem. We show that the $R$-matrix and the translation of these two specific
shifts can be constructed from the solutions of two differential equations that
generalise the classical Airy differential equation. Using this, we prove that
the descendant intersection theory of the shifted Witten classes satisfies
topological recursion on two $1$-parameter families of spectral curves. By
taking the limit as the parameter goes to zero for these families of spectral
curves, we prove that the descendant intersection theory of the Witten $r$-spin
class can be computed by topological recursion on the $r$-Airy spectral curve.
We finally show that this proof suffices to deduce Witten's $r$-spin
conjecture, already proved by Faber, Shadrin and Zvonkine, which claims that
the generating series of $r$-spin intersection numbers is the tau function of
the $r$-KdV hierarchy that satisfies the string equation.


title:
Dark matter-baryon scattering effects on temperature perturbations and
  implications for cosmic dawn

abstract:
  The nature of dark matter remains unknown, but upcoming measurements probing
the high-redshift Universe may provide invaluable insight. In the presence of
dark matter-baryon scattering, the suppression in the matter power spectrum and
the colder mean gas temperature are expected to modify the evolution of cosmic
dawn and reionization. However, the contributions from such interactions to the
baryon and dark matter temperature perturbations have been neglected thus far.
In this work, we derive these contributions, evolve the cosmological
perturbations until the end of the dark ages and show that they may have a
significant impact in the beginning of cosmic dawn. In particular, we find that
the amplitude of the temperature power spectrum at large scales can change by
up to 1--2 orders of magnitude and that the matter power spectrum is further
suppressed with respect to $\Lambda$CDM by $5$-$10\%$ at $k\sim 200\, {\rm
Mpc^{-1}}$ compared to the computation ignoring these contributions for
scattering cross sections at current CMB limits. As a case example, we also
compute the HI power spectrum from the dark ages, finding significant
differences due to the changes in the temperature and ionization fraction power
spectra. We argue that these new contributions must be included in studies of
this dark matter model relying on cosmic dawn and reionization observables.


title:
Probing neutrino magnetic moments and the XENON1T excess with coherent
  elastic solar neutrino scattering

abstract:
  The detection potential of the active neutrino magnetic moment ($\nu_{a}$MM)
and the active sterile transition magnetic moment ($\nu_{s}$MM) of the solar
neutrino CE$\nu$NS process in future Dark Matter direct detection experiments
is studied and compared with the respective allowed range to explain the
Xenon1T excess. We find that the sensitivity to the $\nu_{a}$MM approaches to
the level between $10^{-10}\mu_B$ and $10^{-11}\mu_B$, which is dominantly
limited by the detection threshold. On the other hand, the future solar
neutrino CE$\nu$NS detection would be powerful in probing the $\nu_{s}$MM for
the sterile neutrino mass below 10 MeV, which could unambiguously test the
$\nu_{s}$MM explanation of the Xenon1T excess. The sensitivity in the general
framework with both $\nu_{a}$MM and $\nu_{s}$MM contributions is also derived.


title:
Tracking the evolution from isolated dimers to many-body entanglement in
  NaLu$_x$Yb$_{1-x}$Se$_2$

abstract:
  We synthesize homogeneous compositions of NaLu$_x$Yb$_{1-x}$Se$_2$,
connecting non-magnetic NaLuSe$_2$ to the triangular lattice spin liquid
candidate NaYbSe$_2$. Thermal and magnetic properties are studied as the system
evolves from one with dilute magnetic defects to one of a dense magnetic
lattice. The field and temperature dependent heat capacity show the carriers of
entropy crossover from isolated magnetic ions to a correlated lattice borne
from spin dimers. For the dilute system we estimate the single ion anisotropy
$(g_\perp/g_\parallel =3.13)$ and also the dimer exchange couplings
$J_\parallel(=5.4$~K) and $J_\perp(=9.6$~K), in order to draw comparison to the
half-doped and full magnetic compounds.


title: Exploring Plain Vision Transformer Backbones for Object Detection

abstract:
  We explore the plain, non-hierarchical Vision Transformer (ViT) as a backbone
network for object detection. This design enables the original ViT architecture
to be fine-tuned for object detection without needing to redesign a
hierarchical backbone for pre-training. With minimal adaptations for
fine-tuning, our plain-backbone detector can achieve competitive results.
Surprisingly, we observe: (i) it is sufficient to build a simple feature
pyramid from a single-scale feature map (without the common FPN design) and
(ii) it is sufficient to use window attention (without shifting) aided with
very few cross-window propagation blocks. With plain ViT backbones pre-trained
as Masked Autoencoders (MAE), our detector, named ViTDet, can compete with the
previous leading methods that were all based on hierarchical backbones,
reaching up to 61.3 box AP on the COCO dataset using only ImageNet-1K
pre-training. We hope our study will draw attention to research on
plain-backbone detectors. Code will be made available.


title:
L^3U-net: Low-Latency Lightweight U-net Based Image Segmentation Model
  for Parallel CNN Processors

abstract:
  In this research, we propose a tiny image segmentation model, L^3U-net, that
works on low-resource edge devices in real-time. We introduce a data folding
technique that reduces inference latency by leveraging the parallel
convolutional layer processing capability of the CNN accelerators. We also
deploy the proposed model to such a device, MAX78000, and the results show that
L^3U-net achieves more than 90% accuracy over two different segmentation
datasets with 10 fps.


title:
CaDeX: Learning Canonical Deformation Coordinate Space for Dynamic
  Surface Representation via Neural Homeomorphism

abstract:
  While neural representations for static 3D shapes are widely studied,
representations for deformable surfaces are limited to be template-dependent or
lack efficiency. We introduce Canonical Deformation Coordinate Space (CaDeX), a
unified representation of both shape and nonrigid motion. Our key insight is
the factorization of the deformation between frames by continuous bijective
canonical maps (homeomorphisms) and their inverses that go through a learned
canonical shape. Our novel deformation representation and its implementation
are simple, efficient, and guarantee cycle consistency, topology preservation,
and, if needed, volume conservation. Our modelling of the learned canonical
shapes provides a flexible and stable space for shape prior learning. We
demonstrate state-of-the-art performance in modelling a wide range of
deformable geometries: human bodies, animal bodies, and articulated objects.


title: Learning Instance-Specific Adaptation for Cross-Domain Segmentation

abstract:
  We propose a test-time adaptation method for cross-domain image segmentation.
Our method is simple: Given a new unseen instance at test time, we adapt a
pre-trained model by conducting instance-specific BatchNorm (statistics)
calibration. Our approach has two core components. First, we replace the
manually designed BatchNorm calibration rule with a learnable module. Second,
we leverage strong data augmentation to simulate random domain shifts for
learning the calibration rule. In contrast to existing domain adaptation
methods, our method does not require accessing the target domain data at
training time or conducting computationally expensive test-time model
training/optimization. Equipping our method with models trained by standard
recipes achieves significant improvement, comparing favorably with several
state-of-the-art domain generalization and one-shot unsupervised domain
adaptation approaches. Combining our method with the domain generalization
methods further improves performance, reaching a new state of the art.


title: Understanding 3D Object Articulation in Internet Videos

abstract:
  We propose to investigate detecting and characterizing the 3D planar
articulation of objects from ordinary videos. While seemingly easy for humans,
this problem poses many challenges for computers. We propose to approach this
problem by combining a top-down detection system that finds planes that can be
articulated along with an optimization approach that solves for a 3D plane that
can explain a sequence of observed articulations. We show that this system can
be trained on a combination of videos and 3D scan datasets. When tested on a
dataset of challenging Internet videos and the Charades dataset, our approach
obtains strong performance. Project site:
https://jasonqsy.github.io/Articulation3D


title: Gauged sigma models and exceptional dressing cosets

abstract:
  The Poisson-Lie (PL) T-duality is a generalized T-duality based on the Lie
algebra of the Drinfel'd double. In particular, when we consider the PL
T-duality of a coset space, the dual space is found to be a generalized coset
space, which is called the dressing coset. In this paper, we investigate an
extension of the dressing cosets to the U-duality setup. We propose the gauged
actions for various branes in M-theory and type IIB theory, where the
generalized metric is constructed by using the Exceptional Drinfel'd Algebra
(EDA) and the gauge algebra is a certain isotropic subalgebra of the EDA. By
eliminating the gauge fields, the gauged action reduces to the standard brane
action on a certain reduced background, which we call the exceptional dressing
coset. We also propose an alternative definition of the exceptional dressing
cosets based on Sfetsos's approach and reproduce a known example of non-Abelian
T-duality in the U-duality-covariant formulation.


title: Large-Scale Pre-training for Person Re-identification with Noisy Labels

abstract:
  This paper aims to address the problem of pre-training for person
re-identification (Re-ID) with noisy labels. To setup the pre-training task, we
apply a simple online multi-object tracking system on raw videos of an existing
unlabeled Re-ID dataset "LUPerson" nd build the Noisy Labeled variant called
"LUPerson-NL". Since theses ID labels automatically derived from tracklets
inevitably contain noises, we develop a large-scale Pre-training framework
utilizing Noisy Labels (PNL), which consists of three learning modules:
supervised Re-ID learning, prototype-based contrastive learning, and
label-guided contrastive learning. In principle, joint learning of these three
modules not only clusters similar examples to one prototype, but also rectifies
noisy labels based on the prototype assignment. We demonstrate that learning
directly from raw videos is a promising alternative for pre-training, which
utilizes spatial and temporal correlations as weak supervision. This simple
pre-training task provides a scalable way to learn SOTA Re-ID representations
from scratch on "LUPerson-NL" without bells and whistles. For example, by
applying on the same supervised Re-ID method MGN, our pre-trained model
improves the mAP over the unsupervised pre-training counterpart by 5.7%, 2.2%,
2.3% on CUHK03, DukeMTMC, and MSMT17 respectively. Under the small-scale or
few-shot setting, the performance gain is even more significant, suggesting a
better transferability of the learned representation. Code is available at
https://github.com/DengpanFu/LUPerson-NL


title: A cellular automaton decoder for a noise-bias tailored color code

abstract:
  Self-correcting quantum memories demonstrate robust properties that can be
exploited to improve active quantum error-correction protocols. Here we propose
a cellular automaton decoder for a variation of the color code where the bases
of the physical qubits are locally rotated, which we call the XYZ color code.
The local transformation means our decoder demonstrates key properties of a
two-dimensional fractal code if the noise acting on the system is infinitely
biased towards dephasing, namely, no string-like logical operators. As such, in
the high-bias limit, our local decoder reproduces the behavior of a partially
self-correcting memory. At low error rates, our simulations show that the
memory time diverges polynomially with system size without intervention from a
global decoder, up to some critical system size that grows as the error rate is
lowered. Furthermore, although we find that we cannot reproduce partially
self-correcting behavior at finite bias, our numerics demonstrate improved
memory times at realistic noise biases. Our results therefore motivate the
design of tailored cellular automaton decoders that help to reduce the
bandwidth demands of global decoding for realistic noise models.


title:
Parallel framework for Dynamic Domain Decomposition of Data Assimilation
  problems a case study on Kalman Filter algorithm

abstract:
  We focus on Partial Differential Equation (PDE) based Data Assimilatio
problems (DA) solved by means of variational approaches and Kalman filter
algorithm. Recently, we presented a Domain Decomposition framework (we call it
DD-DA, for short) performing a decomposition of the whole physical domain along
space and time directions, and joining the idea of Schwarz' methods and
parallel in time approaches. For effective parallelization of DD-DA algorithms,
the computational load assigned to subdomains must be equally distributed.
Usually computational cost is proportional to the amount of data entities
assigned to partitions. Good quality partitioning also requires the volume of
communication during calculation to be kept at its minimum. In order to deal
with DD-DA problems where the observations are nonuniformly distributed and
general sparse, in the present work we employ a parallel load balancing
algorithm based on adaptive and dynamic defining of boundaries of DD -- which
is aimed to balance workload according to data location. We call it DyDD. As
the numerical model underlying DA problems arising from the so-called
discretize-then-optimize approach is the constrained least square model (CLS),
we will use CLS as a reference state estimation problem and we validate DyDD on
different scenarios.


title: Recent improvements of ASR models in the face of adversarial attacks

abstract:
  Like many other tasks involving neural networks, Speech Recognition models
are vulnerable to adversarial attacks. However recent research has pointed out
differences between attacks and defenses on ASR models compared to image
models. Improving the robustness of ASR models requires a paradigm shift from
evaluating attacks on one or a few models to a systemic approach in evaluation.
We lay the ground for such research by evaluating on various architectures a
representative set of adversarial attacks: targeted and untargeted,
optimization and speech processing-based, white-box, black-box and targeted
attacks. Our results show that the relative strengths of different attack
algorithms vary considerably when changing the model architecture, and that the
results of some attacks are not to be blindly trusted. They also indicate that
training choices such as self-supervised pretraining can significantly impact
robustness by enabling transferable perturbations. We release our source code
as a package that should help future research in evaluating their attacks and
defenses.


title:
Efficient Localness Transformer for Smart Sensor-Based Energy
  Disaggregation

abstract:
  Modern smart sensor-based energy management systems leverage non-intrusive
load monitoring (NILM) to predict and optimize appliance load distribution in
real-time. NILM, or energy disaggregation, refers to the decomposition of
electricity usage conditioned on the aggregated power signals (i.e., smart
sensor on the main channel). Based on real-time appliance power prediction
using sensory technology, energy disaggregation has great potential to increase
electricity efficiency and reduce energy expenditure. With the introduction of
transformer models, NILM has achieved significant improvements in predicting
device power readings. Nevertheless, transformers are less efficient due to
O(l^2) complexity w.r.t. sequence length l. Moreover, transformers can fail to
capture local signal patterns in sequence-to-point settings due to the lack of
inductive bias in local context. In this work, we propose an efficient
localness transformer for non-intrusive load monitoring (ELTransformer).
Specifically, we leverage normalization functions and switch the order of
matrix multiplication to approximate self-attention and reduce computational
complexity. Additionally, we introduce localness modeling with sparse local
attention heads and relative position encodings to enhance the model capacity
in extracting short-term local patterns. To the best of our knowledge,
ELTransformer is the first NILM model that addresses computational complexity
and localness modeling in NILM. With extensive experiments and quantitative
analyses, we demonstrate the efficiency and effectiveness of the the proposed
ELTransformer with considerable improvements compared to state-of-the-art
baselines.


title:
Machine Learning Approaches for Non-Intrusive Home Absence Detection
  Based on Appliance Electrical Use

abstract:
  Home absence detection is an emerging field on smart home installations.
Identifying whether or not the residents of the house are present, is important
in numerous scenarios. Possible scenarios include but are not limited to:
elderly people living alone, people suffering from dementia, home quarantine.
The majority of published papers focus on either pressure / door sensors or
cameras in order to detect outing events. Although the aforementioned
approaches provide solid results, they are intrusive and require modifications
for sensor placement. In our work, appliance electrical use is investigated as
a means for detecting the presence or absence of residents. The energy use is
the result of power disaggregation, a non intrusive / non invasive sensing
method. Since a dataset providing energy data and ground truth for home absence
is not available, artificial outing events were introduced on the UK-DALE
dataset, a well known dataset for Non Intrusive Load Monitoring (NILM). Several
machine learning algorithms were evaluated using the generated dataset.
Benchmark results have shown that home absence detection using appliance power
consumption is feasible.


title:
Identification of diffracted vortex beams at different propagation
  distances using deep learning

abstract:
  Orbital angular momentum of light is regarded as a valuable resource in
quantum technology, especially in quantum communication and quantum sensing and
ranging. However, the OAM state of light is susceptible to undesirable
experimental conditions such as propagation distance and phase distortions,
which hinders the potential for the realistic implementation of relevant
technologies. In this article, we exploit an enhanced deep learning neural
network to identify different OAM modes of light at multiple propagation
distances with phase distortions. Specifically, our trained deep learning
neural network can efficiently identify the vortex beam's topological charge
and propagation distance with 97% accuracy. Our technique has important
implications for OAM based communication and sensing protocols.


title: Mind the gap: Challenges of deep learning approaches to Theory of Mind

abstract:
  Theory of Mind is an essential ability of humans to infer the mental states
of others. Here we provide a coherent summary of the potential, current
progress, and problems of deep learning approaches to Theory of Mind. We
highlight that many current findings can be explained through shortcuts. These
shortcuts arise because the tasks used to investigate Theory of Mind in deep
learning systems have been too narrow. Thus, we encourage researchers to
investigate Theory of Mind in complex open-ended environments. Furthermore, to
inspire future deep learning systems we provide a concise overview of prior
work done in humans. We further argue that when studying Theory of Mind with
deep learning, the research's main focus and contribution ought to be opening
up the network's representations. We recommend researchers use tools from the
field of interpretability of AI to study the relationship between different
network components and aspects of Theory of Mind.


title: Resilience of Sloshing Cold Fronts against Subsequent Minor Mergers

abstract:
  Minor mergers are common in galaxy clusters. They have the potential to
create sloshing cold fronts (SCFs) in the intracluster medium (ICM) of the
cluster. However, the resilience of SCFs to subsequent minor mergers is
unknown. Here we investigate the extent to which SCFs established by an
off-axis minor merger are disrupted by a subsequent minor merger. We perform a
suite of 13 hydrodynamic + N-body simulations of idealised triple cluster
mergers in which we vary the approach direction and impact parameter of the
tertiary cluster. Except for ~1 Gyr after the first core passage of the
tertiary cluster, clear SCFs are present in all merger configurations.
Subsequent head-on minor mergers reduce the number of SCFs significantly, while
subsequent off-axis minor mergers only moderately reduce the number of SCFs. In
particular, outer (>~500 kpc) SCFs are resilient. The results of this work
indicate that SCFs are easily formed in the course of a minor merger and are
long-lived even if a further minor merger takes place. SCFs thus should be
ubiquitous, but deriving the merger history of a given cluster based on its
observed SCFs might be more complex than previously thought.


title: Towards Multimodal Depth Estimation from Light Fields

abstract:
  Light field applications, especially light field rendering and depth
estimation, developed rapidly in recent years. While state-of-the-art light
field rendering methods handle semi-transparent and reflective objects well,
depth estimation methods either ignore these cases altogether or only deliver a
weak performance. We argue that this is due current methods only considering a
single "true" depth, even when multiple objects at different depths contributed
to the color of a single pixel. Based on the simple idea of outputting a
posterior depth distribution instead of only a single estimate, we develop and
explore several different deep-learning-based approaches to the problem.
Additionally, we contribute the first "multimodal light field depth dataset"
that contains the depths of all objects which contribute to the color of a
pixel. This allows us to supervise the multimodal depth prediction and also
validate all methods by measuring the KL divergence of the predicted
posteriors. With our thorough analysis and novel dataset, we aim to start a new
line of depth estimation research that overcomes some of the long-standing
limitations of this field.


title: Proofs of network quantum nonlocality aided by machine learning

abstract:
  The study of nonlocality in scenarios that depart from the bipartite
Einstein-Podolsky-Rosen setup is allowing to uncover many fundamental features
of quantum mechanics. Recently, an approach to building network-local models
based on machine learning lead to the conjecture that the family of quantum
triangle distributions of [DOI:10.1103/PhysRevLett.123.140401] did not admit
triangle-local models in a larger range than the original proof. We prove part
of this conjecture in the affirmative. Our approach consists in reducing the
family of original, four-outcome distributions to families of binary-outcome
ones, which we prove not to admit triangle-local models by means of the
inflation technique. In the process, we produce a large collection of network
Bell inequalities for the triangle scenario with binary outcomes, which are of
independent interest.


title: Binary black hole mergers from merged stars in the galactic field

abstract:
  The majority of massive stars are found in close binaries which: (i) are
prone to merge and (ii) are accompanied by another distant tertiary star
(triples). Here, we study the evolution of the stellar post-merger binaries
composed of the merger product and the tertiary companion. We find that
post-merger binaries originating from compact stellar triples with outer
semi-major axes $a_{\rm out,init}\lesssim10^1\,-\,10^2\,\rm AU$ provide a new
way to form binary black hole mergers in the galactic field. By means of a
population synthesis, we estimate their contribution to the total black hole
merger rate to be $\mathcal{R}(z=0)=0.3\,-\,25.2\,\rm Gpc^{-3}\,yr^{-1}$.
Merging binary black holes that form from stellar post-merger binaries have
exceptionally low mass ratios. We identify a critical mass ratio $q\simeq0.5$
below which they dominate the total black hole merger rate in the field. We
show that after including their additional contribution, the mass ratio
distribution of binary black hole mergers in the galactic field scenario is in
better agreement with that inferred from gravitational wave detections.

categories: astro-ph.GA astro-ph.HE astro-ph.SR gr-qc


title:
Constraints on galaxy formation from the
  cosmic-infrared-background$\,-\,$optical-imaging cross-correlation using
  $\boldsymbol{Herschel}$ and UNIONS

abstract:
  Using $\it{Herschel}$-SPIRE imaging and the Canada-France Imaging Survey
(CFIS) Low Surface Brightness data products from the Ultraviolet Near-Infrared
Optical Northern Survey (UNIONS), we present a cross-correlation between the
cosmic infrared background and cosmic optical background fluctuations. With a
combined sky area of $91\,{\rm deg}^2$ and a minimum resolved scale of
$18\,$arcsec, the cross-spectrum is measured for two cases: all galaxies are
kept in the images; or all individually-detected galaxies are masked to produce
`background' maps. We report the detection of the cross-correlation signal at
$\gtrsim 22\,\sigma$ ($\gtrsim 18\,\sigma$ for the background map). The part of
the optical brightness variations that are correlated with the submm emission
translates to an rms brightness of $\simeq 32.5\,{\rm mag}\,{\rm arcsec}^{-2}$
in the $r$ band, a level normally unreachable for individual sources. A
critical issue is determining what fraction of the cross-power spectrum might
be caused by emission from Galactic cirrus. For one of the fields, the Galactic
contamination is approximately a factor of 10 higher than the extragalactic
signal, with the contamination being estimated using a linear regression from
several external survey maps; however, for the other fields, the contamination
is typically around 20 per cent. An additional discriminant is that the
cross-power spectrum is of the approximate form $P(k)\propto 1/k$, much
shallower than that of Galactic cirrus. We interpret the results in a
halo-model framework, which shows good agreement with independent measurements
for the scalings of star-formation rates in galaxies. The approach presented in
this study holds great promise for future surveys such as FYST/CCAT-prime
combined with ${\it Euclid}$ or the Vera Rubin Observatory (LSST), which will
enable a detailed exploration of the evolution of star formation in galaxies.


title:
"My Rhodopsin!": Why Adding Dark Mode to Journals Could Make Us All
  Better Astronomers

abstract:
  The digital age has sparked a revival in the use of "dark mode" (DM) design
in many everyday applications as well as text editors and integrated developer
environments. We present the case for adding a DM theme to astronomical
journals, including a modified class file that generates the theme you see here
as a potential option. DM themes have many beneficial attributes to a user such
as saving battery power and reducing screen burn-in on devices with OLED
screens, increasing figure hopping efficiency, pairing well with
colorblind-friendly palettes, and limiting rhodopsin loss while observing. We
analyzed iPoster design trends from AAS 237 and 238 to gauge the possible
reception of our DM theme, and we estimate that at least 35%, but likely closer
to 42%, of the community would welcome this addition to journals. There are
some drawbacks to using a DM theme when reading papers, including increased ink
usage when reading in a print medium and some diminished legibility and
comprehension in low-light conditions. While these issues are not negligible,
we believe they can be mitigated, especially with a paired submission of both a
DM and traditional, "light mode" manuscript. It is also likely that many of us
will become better astronomers as a result of adding DM to journals.


title:
Great Balls of FIRE II: The evolution and destruction of star clusters
  across cosmic time in a Milky Way-mass galaxy

abstract:
  The current generation of galaxy simulations can resolve individual giant
molecular clouds, the progenitors of dense star clusters. But the evolutionary
fate of these young massive clusters (YMCs), and whether they can become the
old globular clusters (GCs) observed in many galaxies, is determined by a
complex interplay of internal dynamical processes and external galactic
effects. We present the first star-by-star $N$-body models of massive
($N\sim10^5-10^7$) star clusters formed in a FIRE-2 MHD simulation of a Milky
Way-mass galaxy, with all of the relevant initial conditions and galactic tidal
effects extracted directly from the cosmological simulation. We randomly select
895 ($\sim 30\%$) of the YMCs with $ > 6\times10^4M_{\odot}$ from Grudi\'c et
al. 2022 and integrate them to the present day using the Cluster Monte Carlo
Code, CMC. This procedure predicts a MW-like system with 148 GCs, most of which
were formed during the early, bursty mode of star formation in the galaxy. Our
GCs are younger, less massive, and more core collapsed than clusters in the
Milky Way or M31. This is a direct result of the assembly history and
age-metallicity relationship of the GCs' host galaxy: younger clusters are
preferentially born in stronger galactic tidal fields and initially retain
fewer stellar-mass black holes, causing them to lose mass faster and reach core
collapse sooner than their older counterparts. Our results suggest that the
masses and core/half-light radii of GCs are shaped not only by internal
dynamical processes, but by the specific evolutionary history of their host
galaxies as well. These results emphasize that $N$-body studies with realistic
stellar physics are crucial to understanding the evolution and present-day
properties of galactic GC systems.


title: Critical metallic phase in the overdoped random $t$-$J$ model

abstract:
  We investigate a model of electrons with random and all-to-all hopping and
spin exchange interactions, with a constraint of no double occupancy. The model
is studied in a SYK-like large-$M$ limit with SU($M$) spin symmetry. We use
numerical studies on both real and imaginary frequency axes, along with
asymptotic analyses, to establish the existence of a critical non-Fermi-liquid
metallic ground state at large doping, with the spin correlation exponent
varying with doping. This critical solution possesses a time-reparametrization
symmetry, akin to SYK models, leading to a linear-in-temperature resistivity
over the full range of doping the solution is present. It is therefore an
attractive mean-field description of the overdoped region of cuprates, where
experiments have observed a linear-$T$ resistivity in a broad region. The
critical metal also displays a strong particle-hole asymmetry, which is
relevant to Seebeck coefficient measurements. We show that the critical metal
has an instability to a low-doping spin-glass phase, and compute a critical
doping value. We also describe the properties of this metallic spin-glass
phase.


title:
Exponential Approach to the Hydrodynamic Attractor in Yang-Mills Kinetic
  Theory

abstract:
  We use principal component analysis to study the hydrodynamic attractor in
Yang-Mills kinetic theory undergoing the Bjorken expansion with Color Glass
Condensate initial conditions. The late time hydrodynamic attractor is
characterized by a single principal component determining the overall energy
scale. How it is reached is governed by the disappearance of single subleading
principal component characterizing deviations of the pressure anisotropy, the
screening mass and the scattering rate. We find that for wide range of
couplings the approach to the hydrodynamic attractor at late times is well
described by an exponential. Its decay rate dependence on the coupling turns
out to translate into a simple dependence on the shear viscosity to entropy
density ratio.


title:
Dynamical consequences of 1-form symmetries and the exceptional
  Argyres-Douglas theories

abstract:
  Higher-form symmetries have proved useful in constraining the dynamics of a
number of quantum field theories. In the context of the Argyres-Douglas (AD)
theories of the $(G,G')$ type, we find that the 1-form symmetries are invariant
under the Higgs branch flow, and that they are captured by the non-Higgsable
sector at a generic point on the Higgs branch of the AD theory in question. As
a consequence, dimensional reduction of an AD theory with a non-trivial 1-form
symmetry to 3d leads to a free sector. We utilize these observations, along
with other results, to propose systematically the mirror theories for the AD
theories of the $(A_n, E_m)$ type. As a by-product of these findings, we
discover many important results: the Flip-Flip duality for all $T[G]$ theories
with simply-laced group $G$, including the exceptional ones; the class
$\mathcal{S}$ descriptions of exceptional affine Dynkin diagram such that all
gauge groups are special unitary; the universality of the mirror theories for
$D_{h^\vee_G}(G)$ with $h^\vee_G$ the dual Coxeter number of $G$; and the
triviality of the 2-group structure in the $(A_n, E_m)$ theories.


title:
Worry No More, The Hubble Tension is Relieved: A Truly Direct
  Measurement of the Hubble Constant from Mooniversal Expansion

abstract:
  Using sedimentary and eclipse-based measurements of the lunar recession
velocity, we derive a new local-Universe measurement of the Hubble constant
($H_0$) from the recession rate of Earth's Moon. Taking into account the
effects of tides, we find a value of $H_{0}$ = 63.01 $\pm$ 1.79 km s$^{-1}$
Mpc$^{-1}$, which is in approximate agreement with the Planck space mission's
measurement using the cosmic microwave background (CMB) and base $\Lambda$CDM.
Our new measurement represents the first ever model-independent, single-step
measurement of the Universe's current expansion rate. This is also the first
major local Universe measurement of $H_0$ which is below the measurement from
Planck. Importantly, it is robust to the systematic errors that may be present
in other $H_0$ measurements using other cosmological probes such as type Ia
supernovae, baryon acoustic oscillations, or lensed quasars. Our work provides
key evidence towards the notion that the existing Hubble tension may indeed be
a result of systematic uncertainties in the local distance ladder.


title:
The OGLE Collection of Variable Stars. Nearly 66,000 Mira stars in the
  Milky Way

abstract:
  We present a collection of 65,981 Mira-type variable stars found in the
Optical Gravitational Lensing Experiment (OGLE) project database. Two-thirds of
our sample (40,356 objects) are located in the Galactic bulge fields, whereas
25,625 stars are in the Galactic disk. The vast majority of the collection
(47,532 objects) are new discoveries. We provide basic observational parameters
of the Mira variables: equatorial coordinates, pulsation periods, $I$-band and
$V$-band mean magnitudes, $I$-band brightness amplitudes, and identifications
in other catalogs of variable stars. We also provide the $I$-band and $V$-band
time-series photometry collected since 1997 during the OGLE-II, OGLE-III, and
OGLE-IV phases. The classical selection process, i.e., mostly based on the
visual inspection of light curves by experienced astronomers, led us to the
high purity of the catalog. As a result, this collection can be used as a
training set in the machine learning classification algorithms. Using
overlapping parts of adjacent OGLE fields, we estimate the completeness of the
catalog to be about 96%. We compare and discuss the statistical features of
Miras located in different regions of the Milky Way. We show examples of stars
that change their type over time, from a semi-regular variable to Mira and vice
versa. This dataset is perfectly suited to study the three-dimensional
structure of the Milky Way, and it may help to explain the puzzle of the
X-shaped bulge.


title: Optimisation of electrically-driven multi-donor quantum dot qubits

abstract:
  Multi-donor quantum dots have been at the forefront of recent progress in
Si-based quantum computation. Among them, $2P:1P$ qubits have a built-in dipole
moment, enabling all-electrical spin operation via hyperfine mediated electron
dipole spin resonance (EDSR). The development of all-electrical multi-donor dot
qubits requires a full understanding of their EDSR and coherence properties,
incorporating multi-valley nature of their ground state. Here, by introducing a
variational effective mass wave-function, we examine the impact of qubit
geometry and nearby charge defects on the electrical operation and coherence of
$2P:1P$ qubits. We report four outcomes: (i) The difference in the hyperfine
interaction between the $2P$ and $1P$ sites enables fast EDSR, with $T_\pi \sim
10-50$ ns and a Rabi ratio $ (T_1/T_\pi) \sim 10^6$. We analyse qubits with the
$2P:1P$ axis aligned along the [100], [110] and [111] crystal axes, finding
that the fastest EDSR time $T_\pi$ occurs when the $2P:1P$ axis is
$\parallel$[111], while the best Rabi ratio occurs when it is $\parallel$
[100]. This difference is attributed to the difference in the wave function
overlap between $2P$ and $1P$ for different geometries. In contrast, the choice
of $2P$ axis has no visible impact on qubit operation. (ii) Sensitivity to
random telegraph noise due to nearby charge defects depends strongly on the
location of the nearby defects with respect to the qubit. For certain
orientations of defects random telegraph noise has an appreciable effect both
on detuning and $2P-1P$ tunneling, with the latter inducing gate errors. (iii)
The qubit is robust against $1/f$ noise provided it is operated away from the
charge anticrossing. (iv) Entanglement via exchange is several orders of
magnitude faster than dipole-dipole coupling. These findings pave the way
towards fast, low-power, coherent and scalable donor dot-based quantum
computing.


title:
Importance of source structure on complex organics emission II. Can
  disks explain lack of methanol emission from some low-mass protostars?

abstract:
  Some protostellar systems show little or no millimetre line emission of
complex organics. This can be interpreted as a low abundance of these
molecules, alternatively they could be present in the system but are not seen
in the gas. The goal is to investigate the latter hypothesis for methanol. We
will attempt to answer the question: Does the presence of a disk and optically
thick dust reduce methanol emission even if methanol is abundant in the ices
and gas? Using the radiative transfer code RADMC-3D, methanol emission lines
from an envelope-only model and an envelope-plus-disk model are calculated and
compared with each other and the observations. Methanol emission from the
envelope-only model is always stronger than from the envelope-plus-disk model
by at least a factor 2 as long as the disk radius is larger than 30 au (for L=8
L$_{\odot}$). In most cases, this is due to lower temperatures and, hence, the
smaller amount of warm methanol inside the snow surface of the
envelope-plus-disk model. The intensities drop by more than an order of
magnitude for models including high mm opacity dust grains and disk radii of at
least 50 au (for L=8 L$_{\odot}$) due to continuum over-subtraction. The line
intensities from the envelope-only models overproduce the observations of
protostars with lower methanol emission even with large dust optical depth
effects. The envelope-plus-disk models can explain the bulk of the
observations. However, they can only reproduce the observations of sources with
high luminosities and low methanol emission when dust optical depth effects
especially continuum over-subtraction in the disk becomes effective. Therefore,
both the effects of disk and dust optical depth should be considered to explain
the observations. In conclusion: Absence of methanol emission does not imply
absence of methanol molecules in either gas or ice.


title: Entanglement dynamics of random quantum channels

abstract:
  The process by which open quantum systems thermalize with an environment is
both of fundamental interest and relevant to noisy quantum devices. As a
minimal model of this process, we consider a qudit chain evolving under local
random unitaries and local depolarization channels. After mapping to a
statistical mechanics model, the depolarization acts like a symmetry-breaking
field, and we argue that it causes the system to thermalize within a time scale
independent of system size. We show that various bipartite entanglement
measures--mutual information, operator entanglement, and entanglement
negativity--grow at a speed proportional to the size of the bipartition
boundary. As a result, these entanglement measures obey an area law: their
maximal value during the dynamics is bounded by the boundary instead of the
volume. In contrast, if the depolarization only acts at the system boundary,
then the maximum value of the entanglement measures obeys a volume law. We
complement our analysis with scalable simulations involving Clifford gates, for
both one and two-dimensional systems.


title:
The Latest Constraints on Inflationary B-modes from the BICEP/Keck
  Telescopes

abstract:
  For the past decade, the BICEP/Keck collaboration has been operating a series
of telescopes at the Amundsen-Scott South Pole Station measuring degree-scale
$B$-mode polarization imprinted in the Cosmic Microwave Background (CMB) by
primordial gravitational waves (PGWs). These telescopes are compact refracting
polarimeters mapping about 2% of the sky, observing at a broad range of
frequencies to account for the polarized foreground from Galactic synchrotron
and thermal dust emission. Our latest publication "BK18" utilizes the data
collected up to the 2018 observing season, in conjunction with the publicly
available WMAP and Planck data, to constrain the tensor-to-scalar ratio $r$. It
particularly includes (1) the 3-year BICEP3 data which is the current deepest
CMB polarization map at the foreground-minimum 95 GHz; and (2) the Keck 220 GHz
map with a higher signal-to-noise ratio on the dust foreground than the Planck
353 GHz map. We fit the auto- and cross-spectra of these maps to a
multicomponent likelihood model ($\Lambda$CDM+dust+synchrotron+noise+$r$) and
find it to be an adequate description of the data at the current noise level.
The likelihood analysis yields $\sigma(r)=0.009$. The inference of $r$ from our
baseline model is tightened to $r_{0.05}=0.014^{+0.010}_{-0.011}$ and
$r_{0.05}<0.036$ at 95% confidence, meaning that the BICEP/Keck $B$-mode data
is the most powerful existing dataset for the constraint of PGWs. The up-coming
BICEP Array telescope is projected to reach $\sigma(r) \lesssim 0.003$ using
data up to 2027.


title:
COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical
  Image Segmentation based on Target-aware Domain Translation and Iterative
  Self-Training

abstract:
  Recent advances in deep learning-based medical image segmentation studies
achieve nearly human-level performance when in fully supervised condition.
However, acquiring pixel-level expert annotations is extremely expensive and
laborious in medical imaging fields. Unsupervised domain adaptation can
alleviate this problem, which makes it possible to use annotated data in one
imaging modality to train a network that can successfully perform segmentation
on target imaging modality with no labels. In this work, we propose a
self-training based unsupervised domain adaptation framework for 3D medical
image segmentation named COSMOS and validate it with automatic segmentation of
Vestibular Schwannoma (VS) and cochlea on high-resolution T2 Magnetic Resonance
Images (MRI). Our target-aware contrast conversion network translates source
domain annotated T1 MRI to pseudo T2 MRI to enable segmentation training on
target domain, while preserving important anatomical features of interest in
the converted images. Iterative self-training is followed to incorporate
unlabeled data to training and incrementally improve the quality of
pseudo-labels, thereby leading to improved performance of segmentation. COSMOS
won the 1\textsuperscript{st} place in the Cross-Modality Domain Adaptation
(crossMoDA) challenge held in conjunction with the 24th International
Conference on Medical Image Computing and Computer Assisted Intervention
(MICCAI 2021). It achieves mean Dice score and Average Symmetric Surface
Distance of 0.871(0.063) and 0.437(0.270) for VS, and 0.842(0.020) and
0.152(0.030) for cochlea.


title: Universal relations for rotating Boson Stars

abstract:
  Boson stars represent a hypothetical exotic type of compact stellar object
that may be observed from the gravitational signal of coalescing binaries in
current and future GW detectors. In this work we show that the moment of
inertia $I$, the (dimensionless) angular momentum $\chi$ and the quadrupole
moment $Q$ of rotating boson stars obey a universal relation, valid for a wide
set of boson star models. Further, the obtained $I-\chi-Q~$ relation clearly
differs from its famous neutron star counterpart, providing us with an
unequivocal diagnostic tool to distinguish boson stars from ordinary compact
stars or other celestial bodies in GW observations. Such universal (i.e.
model-independent) relations also provide a useful tool to probe the strong
gravity regime of general relativity and to constrain the equation of state of
matter inside compact stars.


title:
Fast Neutrino Conversion in Hydrodynamic Simulations of Neutrino-Cooled
  Accretion Disks

abstract:
  The outflows from neutrino-cooled black-hole (BH) accretion disks formed in
neutron-star mergers or cores of collapsing stars are expected to be
neutron-rich enough to explain a large fraction of elements created by the
rapid neutron-capture (r-) process, but their precise chemical composition
remains elusive. Here, we investigate the role of fast neutrino flavor
conversion, motivated by the findings of our post-processing analysis that
shows evidence of electron-neutrino lepton-number (ELN) crossings deep inside
the disk, hence suggesting possibly non-trivial effects due to neutrino flavor
mixing. We implement a parametric, dynamically self-consistent treatment of
fast conversion in time-dependent simulations and examine the impact on the
disk and its outflows. By activating the, otherwise inefficient, emission of
heavy-lepton neutrinos, fast conversions enhance the disk cooling rates and
reduce the absorption rates of electron-type neutrinos, causing a reduction of
the electron fraction in the disk by 0.03-0.06 and in the ejected material by
0.01-0.03. The r-process yields are enhanced by typically no more than a factor
of two, rendering the overall impact of fast conversions modest. The kilonova
is prolonged as a net result of increased lanthanide opacities and enhanced
radioactive heating rates. We observe only mild sensitivity to the disk mass,
the condition for the onset of flavor conversion, and to the considered cases
of flavor mixing. Remarkably, parametric models of flavor mixing that conserve
the lepton numbers per family result in an overall smaller impact than models
invoking three-flavor equipartition, often assumed in previous works.