title:
CrossFormer: Cross Spatio-Temporal Transformer for 3D Human Pose
  Estimation

abstract:
  3D human pose estimation can be handled by encoding the geometric
dependencies between the body parts and enforcing the kinematic constraints.
Recently, Transformer has been adopted to encode the long-range dependencies
between the joints in the spatial and temporal domains. While they had shown
excellence in long-range dependencies, studies have noted the need for
improving the locality of vision Transformers. In this direction, we propose a
novel pose estimation Transformer featuring rich representations of body joints
critical for capturing subtle changes across frames (i.e., inter-feature
representation). Specifically, through two novel interaction modules;
Cross-Joint Interaction and Cross-Frame Interaction, the model explicitly
encodes the local and global dependencies between the body joints. The proposed
architecture achieved state-of-the-art performance on two popular 3D human pose
estimation datasets, Human3.6 and MPI-INF-3DHP. In particular, our proposed
CrossFormer method boosts performance by 0.9% and 0.3%, compared to the closest
counterpart, PoseFormer, using the detected 2D poses and ground-truth settings
respectively.


title:
SuperScreen: An open-source package for simulating the magnetic response
  of two-dimensional superconducting devices

abstract:
  Quantitative understanding of the spatial distribution of magnetic fields and
Meissner screening currents in two-dimensional (2D) superconductors and
mesoscopic thin film superconducting devices is critical to interpreting the
results of magnetic measurements of such systems. Here, we introduce
SuperScreen, an open-source Python package for simulating the response of 2D
superconductors to trapped flux and applied time-independent or quasi-DC
magnetic fields for any value of the effective magnetic penetration depth,
$\Lambda$. Given an applied magnetic field, SuperScreen solves the 2D London
equation using an efficient matrix inversion method to obtain the Meissner
currents and magnetic fields in and around structures composed of one or more
superconducting thin films of arbitrary geometry. SuperScreen can be used to
model screening effects and calculate self- and mutual-inductance in
superconducting devices, and simulate the magnetic response of inhomogeneous 2D
superconductors.


title:
Gas disk interactions, tides and relativistic effects in the rocky
  planet formation at the substellar mass limit

abstract:
  Our main goal is to study the formation of rocky planets and the first
$100~\textrm{Myr}$ of their dynamical evolution around a star with mass $0.08
M_\odot$ close to the substellar mass limit. We developed two sets of $N$-body
simulations assuming an embryo population affected by tidal and general
relativistic effects refined by the inclusion of the spin-up and contraction of
the central star, and immerse in a gas disk during the first 10 Myr. Each set
of simulations incorporates a different prescription from literature to
calculate the interaction between the gas-disk and the embryos: one widely used
prescription based on results from hydrodynamics simulations, and a recent
prescription based on the analytic treatment of dynamical friction. We found
that given a standard disk model, the dynamical evolution and the final
architectures of the resulting rocky planets is strongly related with the
prescription used to treat the interaction within the gas and the embryos,
having a big impact on the resulting close-in planet population and
particularly on those located inside the habitable zone. We found a good
agreement within the distribution of period ratio of adjacent confirmed
exoplanets observed around very low mass stars and brown dwarfs and those
obtained from our simulations only when the prescription based on dynamical
friction for gas-embryo interaction is used. Our results also reproduce a
close-in planet population of interest located inside the habitable zone. A
fraction of those planets will be exposed for a long period of time to the
stellar irradiation inside the inner edge of the evolving habitable zone until
the zone reaches them.


title:
Probabilistic Analysis of Aircraft Using Multi-Fidelity Aerodynamics
  Databases

abstract:
  The rise in computational capability has increased reliance on simulations to
inform aircraft design. However aircraft airworthiness testing for flight
certification remains rooted in real-world experiments performed after
manufacturing an aircraft prototype. Leveraging multi-fidelity modeling and
uncertainty quantification, we present a framework creating a stochastic
representation of the aircraft, uses it to simulate flight certification
maneuvers, and determines the likelihood of successfully meeting the
certification requirement. We focus on uncertainties associated with
Computational Fluid Dynamics simulations solving the Reynolds-Averaged
Navier-Stokes equations. The simulation predictions and associated
uncertainties are combined with data from other analysis tools to create
stochastic aerodynamics and controls databases. The databases describe the
aircraft's behavior across its flight envelope and provide probability
distributions for its predictions. Databases are generated for two aircraft
configurations, the National Aeronautics and Space Administration (NASA) Common
Research Model and the Generic T-tail Transport aircraft. Samples from the
databases, representing different aircraft behavior, are created. Each sample
is run through a flight simulation representing a real-world airworthiness test
performed by the Federal Aviation Administration (FAA). These tests are
agglomerated to create distributions of the performance metrics, quantifying
the probability that the aircraft succeeds in performing the certification
maneuver. Simulating flight certification testing before building a full-size
aircraft prototype mitigates the enormous costs of expensive redesigns late in
the aircraft design process. The calculation of the failure rates provides
design suggestions to ensure the aircraft can meet the certification
requirement with a prescribed success rate.


title:
An account on links between Finsler and Lorentz Geometries for
  Riemannian Geometers

abstract:
  Some links between Lorentz and Finsler geometries have been developed in the
last years, with applications even to the Riemannian case. Our purpose is to
give a brief description of them, which may serve as an introduction to recent
references. As a motivating example, we start with Zermelo navigation problem,
where its known Finslerian description permits a Lorentzian picture which
allows for a full geometric understanding of the original problem. Then, we
develop some issues including: (a) the accurate description of the Lorentzian
causality using Finsler elements, (b) the non-singular description of some
Finsler elements (such as Kropina metrics or complete extensions of Randers
ones with constant flag curvature), (c) the natural relation between the
Lorentzian causal boundary and the Gromov and Busemann ones in the Finsler
setting, and (d) practical applications to the propagation of waves and
firefronts.


title:
Automated Algorithm Selection: from Feature-Based to Feature-Free
  Approaches

abstract:
  We propose a novel technique for algorithm-selection, applicable to
optimisation domains in which there is implicit sequential information
encapsulated in the data, e.g., in online bin-packing. Specifically we train
two types of recurrent neural networks to predict a packing heuristic in online
bin-packing, selecting from four well-known heuristics. As input, the RNN
methods only use the sequence of item-sizes. This contrasts to typical
approaches to algorithm-selection which require a model to be trained using
domain-specific instance features that need to be first derived from the input
data. The RNN approaches are shown to be capable of achieving within 5% of the
oracle performance on between 80.88% to 97.63% of the instances, depending on
the dataset. They are also shown to outperform classical machine learning
models trained using derived features. Finally, we hypothesise that the
proposed methods perform well when the instances exhibit some implicit
structure that results in discriminatory performance with respect to a set of
heuristics. We test this hypothesis by generating fourteen new datasets with
increasing levels of structure, and show that there is a critical threshold of
structure required before algorithm-selection delivers benefit.


title:
Critical Sets of Solutions of Elliptic Equations in Periodic
  Homogenization

abstract:
  In this paper we study critical sets of solutions $u_\e$ of second-order
elliptic equations in divergence form with rapidly oscillating and periodic
coefficients. We show that the $(d-2)$-dimensional Hausdorff measures of the
critical sets are bounded uniformly with respect to the period $\e$, provided
that doubling indices for solutions are bounded. The key step is an estimate of
"turning " for the projection of a non-constant solution $u_\e$ onto the
subspace of spherical harmonics of order $\ell$, when the doubling index for
$u_\e$ on a sphere $\partial B(0, r)$ is trapped between $\ell -\delta$ and
$\ell +\delta$, for $r$ between $1$ and a minimal radius $r^*\ge C_0\e$. This
estimate is proved by using harmonic approximation successively. With a
suitable $L^2$ renormalization as well as rescaling we are able to control the
accumulated errors introduced by homogenization and projection. Our proof also
gives uniform bounds for Minkowski contents of the critical sets.


title: Point2Seq: Detecting 3D Objects as Sequences

abstract:
  We present a simple and effective framework, named Point2Seq, for 3D object
detection from point clouds. In contrast to previous methods that normally
{predict attributes of 3D objects all at once}, we expressively model the
interdependencies between attributes of 3D objects, which in turn enables a
better detection accuracy. Specifically, we view each 3D object as a sequence
of words and reformulate the 3D object detection task as decoding words from 3D
scenes in an auto-regressive manner. We further propose a lightweight
scene-to-sequence decoder that can auto-regressively generate words conditioned
on features from a 3D scene as well as cues from the preceding words. The
predicted words eventually constitute a set of sequences that completely
describe the 3D objects in the scene, and all the predicted sequences are then
automatically assigned to the respective ground truths through similarity-based
sequence matching. Our approach is conceptually intuitive and can be readily
plugged upon most existing 3D-detection backbones without adding too much
computational overhead; the sequential decoding paradigm we proposed, on the
other hand, can better exploit information from complex 3D scenes with the aid
of preceding predicted words. Without bells and whistles, our method
significantly outperforms previous anchor- and center-based 3D object detection
frameworks, yielding the new state of the art on the challenging ONCE dataset
as well as the Waymo Open Dataset. Code is available at
\url{https://github.com/ocNflag/point2seq}.


title:
Using Reinforcement Learning to Study Platform Economies under Market
  Shocks

abstract:
  Driven by rapid digitization and expansive internet access, market-driven
platforms (e.g., Amazon, DoorDash, Uber, TaskRabbit) are increasingly prevalent
and becoming key drivers of the economy. Across many industries, platforms
leverage digital infrastructure to efficiently match producers and consumers,
dynamically set prices, and enable economies of scale. This increasing
prominence makes it important to understand the behavior of platforms, which
induces complex phenomenon especially in the presence of severe market shocks
(e.g., during pandemics). In this work, we develop a multi-agent simulation
environment to capture key elements of a platform economy, including the kinds
of economic shocks that disrupt a traditional, off-platform market. We use deep
reinforcement learning (RL) to model the pricing and matching behavior of a
platform that optimizes for revenue and various socially-aware objectives. We
start with tractable motivating examples to establish intuitions about the
dynamics and function of optimal platform policies. We then conduct extensive
empirical simulations on multi-period environments, including settings with
market shocks. We characterize the effect of a platform on the efficiency and
resilience of an economic system under different platform design objectives. We
further analyze the consequences of regulation fixing platform fees, and study
the alignment of a revenue-maximizing platform with social welfare under
different platform matching policies. As such, our RL-based framework provides
a foundation for understanding platform economies under different designs and
for yielding new economic insights that are beyond analytical tractability.


title:
HetSched: Quality-of-Mission Aware Scheduling for Autonomous Vehicle
  SoCs

abstract:
  Systems-on-Chips (SoCs) that power autonomous vehicles (AVs) must meet
stringent performance and safety requirements prior to deployment. With
increasing complexity in AV applications, the system needs to meet these
real-time demands of multiple safety-critical applications simultaneously. A
typical AV-SoC is a heterogeneous multiprocessor consisting of accelerators
supported by general-purpose cores. Such heterogeneity, while needed for
power-performance efficiency, complicates the art of task scheduling.
  In this paper, we demonstrate that hardware heterogeneity impacts the
scheduler's effectiveness and that optimizing for only the real-time aspect of
applications is not sufficient in AVs. Therefore, a more holistic approach is
required -- one that considers global Quality-of-Mission (QoM) metrics, as
defined in the paper. We then propose HetSched, a multi-step scheduler that
leverages dynamic runtime information about the underlying heterogeneous
hardware platform, along with the applications' real-time constraints and the
task traffic in the system to optimize overall mission performance. HetSched
proposes two scheduling policies: MSstat and MSdyn and scheduling optimizations
like task pruning, hybrid heterogeneous ranking and rank update. HetSched
improves overall mission performance on average by 4.6x, 2.6x and 2.6x when
compared against CPATH, ADS and 2lvl-EDF (state-of-the-art real-time schedulers
built for heterogeneous systems), respectively, and achieves an average of
53.3% higher hardware utilization, while meeting 100% critical deadlines for
real-world applications of autonomous vehicles. Furthermore, when used as part
of an SoC design space exploration loop, in comparison to prior schedulers,
HetSched reduces the number of processing elements required by an SoC to safely
complete AV's missions by 35% on average while achieving 2.7x lower
energy-mission time product.


title:
GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate
  Degradation of Artificial Neural Language Models

abstract:
  Deep learning (DL) techniques involving fine-tuning large numbers of model
parameters have delivered impressive performance on the task of discriminating
between language produced by cognitively healthy individuals, and those with
Alzheimer's disease (AD). However, questions remain about their ability to
generalize beyond the small reference sets that are publicly available for
research. As an alternative to fitting model parameters directly, we propose a
novel method by which a Transformer DL model (GPT-2) pre-trained on general
English text is paired with an artificially degraded version of itself (GPT-D),
to compute the ratio between these two models' \textit{perplexities} on
language from cognitively healthy and impaired individuals. This technique
approaches state-of-the-art performance on text data from a widely used "Cookie
Theft" picture description task, and unlike established alternatives also
generalizes well to spontaneous conversations. Furthermore, GPT-D generates
text with characteristics known to be associated with AD, demonstrating the
induction of dementia-related linguistic anomalies. Our study is a step toward
better understanding of the relationships between the inner workings of
generative neural language models, the language that they produce, and the
deleterious effects of dementia on human speech and language characteristics.


title: V889 Her: abrupt changes in the magnetic field or differential rotation?

abstract:
  We have applied Zeeman-Doppler imaging (ZDI) to an extensive
spectropolarimetric HARPSpol data set of the magnetically active young solar
analogue V889 Her, covering 35 spectra obtained during six nights in May 2011.
The data set allows us to study Stokes V profiles of the star at almost
identical rotational phases, separated by one or more stellar rotations. We use
these data to study if the line profiles evolve from one rotation to the next,
and find that some evolution does indeed occur. We consider two possible
explanations for this: abrupt changes in the large-scale magnetic field or
differential rotation. We find it quite difficult to distinguish between the
two alternatives using ZDI alone, but the most likely explanation seems to be
that both hypotheses are true. Commonly, rapidly rotating stars are assumed to
have only weak differential rotation. If the strong differential rotation of
V889 Her is indeed present, as has been found in other studies as well, it
could indicate that the theoretical and numerical results of differential
rotation still need to be revised. The rapid changes that seem to occur in the
magnetic field indicate that one should be quite cautious when interpreting ZDI
maps constructed from data over long time intervals.


title:
Beam Training and Alignment for RIS-Assisted Millimeter Wave
  Systems:State of the Art and Beyond

abstract:
  Reconfigurable intelligent surface (RIS) has recently emerged as a promising
paradigm for future cellular networks. Specifically, due to its capability in
reshaping the propagation environment, RIS was introduced to address the
blockage issue in millimeter Wave (mmWave) or even Terahertz (THz)
communications. The deployment of RIS, however, complicates the system
architecture and poses a significant challenge for beam training (BT)/ beam
alignment (BA), a process that is required to establish a reliable link between
the transmitter and the receiver. In this article, we first review several
state-of-the-art beam training solutions for RIS-assisted mmWave systems and
discuss their respective advantages and limitations. We also present a new
multi-directional BT method, which can achieve a decent BA performance with
only a small amount of training overhead. Finally, we outline several important
open issues in BT for RIS-assisted mmWave systems.


title:
Wayne State Universitys Dan Zowada Memorial Observatory:
  Characterization and Pipeline of a 0.5 Meter Robotic Telescope

abstract:
  Wayne State University's Dan Zowada Memorial Observatory is a fully robotic
0.5m telescope and imaging system located under the dark skies of New Mexico.
The observatory is particularly suited to time domain astronomy: the
observation of variable objects, such as tidal disruption events, supernovae,
and active galactic nuclei. We have developed a software suite for image
reduction, alignment and stacking, and calculation of absolute photometry in
the Sloan filters used at the telescope. Our pipeline also performs image
subtraction to enable photometry of objects embedded in bright backgrounds such
as galaxies. The 5 sigma detection limit of the Zowada Observatory for
integration of 16 x 90 second exposures is 19.0 magnitude in g-band, 18.1
magnitude in r-band, 17.9 magnitude in i-band, and 16.6 magnitude in z-band.
For a 3 sigma detection limit, measurements may be performed with greater
uncertainties as deep as 19.9, 19.1. 18.9 and 17.5 magnitude in griz bands,
respectively.


title:
Tensor Perturbations and Thick Branes in Higher Dimensional Gauss-Bonnet
  Gravity

abstract:
  A thick brane model with Gauss-Bonnet action in a homogeneous anisotropic
$D=(4+1+d)$ spacetime is studied. By choosing a concrete metric ansatz, we show
that this spacetime is stable against linear tensor perturbations under certain
conditions. The graviton zero modes are also given. Besides, we examine a
particular example in six-dimensional spacetime with a given warp factor. The
coupled background scalar field and its potential are solved analytically.
Furthermore, the effective potential of the Kaluza-Klein modes of the graviton
is also discussed. We found that the effective potential can have singularities
under certain conditions, which are related to the non-differentiability of the
graviton zero modes.


title: A remark on Jacobi ensemble

abstract:
  We prove the large deviation principle for the supports of Jacobi ensembles
following Guionnet's method.


title:
Simulation Based Inference for Efficient Theory Space Sampling: an
  Application to Supersymmetric Explanations of the Anomalous Muon (g-2)

abstract:
  For the purpose of minimizing the number of sample model evaluations, we
propose and study two algorithms that utilize sequential versions of
likelihood-to-evidence ratio neural estimation. We apply our algorithms to a
supersymmetric interpretation of the anomalous muon magnetic dipole moment in
the context of a phenomenological minimal supersymmetric extension of the
standard model, and recover non-trivial models in an experimentally-constrained
theory space. Finally we summarize further potential possible uses of these
algorithms in future studies.


title:
Statistical Analysis of H I Profile Asymmetry and Shape for Nearby
  Galaxies

abstract:
  We present a uniform analysis of the integrated profile of the H I emission
line of 29,958 nearby ($z < 0.06$) galaxies extracted from the ALFALFA 21 cm
survey. We apply the curve-of-growth technique to derive a database of spectral
parameters and robust estimates of their associated uncertainties. Besides the
central velocity and total flux, the main catalog provides new measures of line
width, profile asymmetry, and profile shape. For a subsample of 13,511 galaxies
with optical properties available from the Sloan Digital Sky Survey, we compute
inclination angle-corrected line widths, rotation velocities empirically
calibrated from spatially resolved observations, and dynamical masses based on
H I sizes estimated from the H I mass. To facilitate subsequent scientific
applications of the database, we also compile a number of ancillary physical
properties of the galaxies, including their optical morphology, stellar mass,
and various diagnostics of star formation activity. We use the homogeneous
catalog of H I parameters to examine the statistical properties of profile
asymmetry and shape. Across the full sample, which covers a wide range of
stellar masses and environments, statistically significant H I profile
asymmetry is detected in $\sim 20\%$ of the galaxy population. The global H I
profiles are $35.2 \pm 0.3\%$ single-peaked, $26.9 \pm 0.3\%$ flat-topped, and
$37.9 \pm 0.3\%$ double-horned. At a given inclination angle, double-horned
profiles are preferentially associated with galaxies of higher stellar mass or
optical concentration, while galaxies of lower mass or concentration tend to
have single-peaked profiles.


title:
Centrally Concentrated H I Distribution Enhances Star Formation in
  Galaxies

abstract:
  We use a sample of 13,511 nearby galaxies from the ALFALFA and SDSS
spectroscopic surveys to study the relation between the spatial distribution of
H I 21 cm emission and star formation rate (SFR). We introduce a new
non-parametric quantity $K$, measured from the curve-of-growth of the line, to
describe the shape of the integrated H I profile. The value of $K$ increases
from double-horned to single-peaked profiles, depending on projection effects
and the spatial and velocity distribution of the gas. Using carefully chosen
samples to control for the competing factors that influence the integrated line
profile, we argue that useful inferences can be made on the spatial
distribution of the gas. We find that galaxies with a high value of $K$ tend to
have more centrally concentrated H I distribution within the optical disk of
the galaxy at fixed conditions, and that larger values of $K$ are associated
with higher levels of total and central SFR. The results suggest that the
global concentration of H I plays an important role in facilitating the
conversion of neutral atomic hydrogen to molecular hydrogen gas, which, in
turn, affects the star formation activity throughout the optical disk. Our
sample is biased against quiescent galaxies, and thus the conclusions may not
hold for galaxies with low SFR or low H I content.


title: Deep follow-up of GW151226: ordinary binary or low-mass-ratio system?

abstract:
  Recent work by Chia et al [arXiv:2105.06486] raises questions about the
nature of the binary black hole event GW151226. While LVK initially determined
this event to be an "ordinary" merger with a modest mass ratio $q \equiv
m_2/m_1 = 7.5 M_\odot / 14 M_\odot$ and modest effective inspiral spin
$\chi_{\rm eff}\approx 0.15$, Chia et al find support for a far more
interesting system: with a significant mass asymmetry $q \approx 4.3 M_\odot /
29 M_\odot$, substantial spin $\chi_{\rm eff}\approx 0.5$, and signs of
Lense-Thirring precession. Chia et al find that the "low-$q$" likelihood peak
is preferred over the high-$q$ peak with a posterior odds of ${\cal O}\approx
96$ while LVK find the low-$q$ peak is disfavored with an odds of ${\cal
O}\approx 8$ (meaning the two studies differ by a ratio of $\approx 770$). This
discrepancy has been challenging to resolve since Chia et al argue that the
low-$q$ peak is present only when the data are analyzed with substantial
computing resources. In this letter, we introduce a "deep follow-up" framework
to efficiently compute the posterior odds between two different points in
parameter space: in this case, one corresponding to the "high-$q$" LVK peak and
one corresponding to the "low-$q$" peak identified by Chia et al. We find the
high-$q$ interpretation is barely preferred with a posterior odds of $\sim1.2$,
suggesting that GW151226 may well be an unusual (low-$q$) event, though, it is
equally well explained as an ordinary merger. This result implies that the LVK
analysis did not adequately sample parameter space. At the same time, we find
less evidence for a low-$q$ peak than reported by Chia et al. We discuss
strategies to produce more reliable parameter estimation studies in
gravitational-wave astronomy.


title:
Weak-coupling to strong-coupling quantum criticality crossover in a
  Kitaev quantum spin liquid $\alpha$-RuCl$_3$

abstract:
  We report an unprecedented quantum criticality crossover representing two
different universal scaling behaviors in a Kitaev quantum magnetic material
$\alpha$-RuCl$_3$. $\alpha$-RuCl$_3$ presents both a symmetry breaking
antiferromagnetic order and a long-range entangled topological order of a
quantum spin liquid, and thus could be a candidate system for a new
universality class involving deconfined fractionalized excitations of the local
Z$_2$ fluxes and itinerant Majorana fermions. Theoretical analyses on the
inelastic neutron scattering and specific heat results demonstrate that
Wilson-Fisher-Yukawa-type 'conventional' weak-coupling quantum criticality in
high energy scales crosses over to heavy-fermion-type 'local' strong-coupling
one in low energy scales. Our findings provide deep insight on how the quantum
criticality evolves in fermion-boson coupled topological systems with different
types of deconfined fermions.


title: Wave propagation and power deposition in blue-core helicon plasma

abstract:
  The wave propagation and power deposition in blue-core helicon plasma are
computed referring to recent experiments. It is found that the radial profile
of wave electric field peaks off-axis during the blue-core formation, and the
location of this peak is very close to that of particle transport barrier
observed in experiment; the radial profile of wave magnetic field shows
multiple radial modes inside the blue-core column, which is consistent with the
experimental observation of coherent high $m$ modes through Bessel function.
The axial profiles of wave field indicate that, once the blue-core mode has
been achieved, waves can only propagate inside the formed column with distinct
phase compared to that outside. The wave energy distribution shows a clear and
sharp boundary at the edge of blue-core column, besides which periodic
structures are observed and the axial periodicity inside is nearly twice that
outside. The dispersion relation inside the blue-core column exhibits multiple
modes, a feature of resonant cavity that selects different modes during
frequency variation, while the dispersion relation outside gives constant wave
number with changed frequency. The power deposition appears to be off-axis in
the radial direction and periodic in the axial direction, and mostly inside the
blue-core column. Analyses based on step-like function theory and introduced
blue-core constant provide consistent results. The equivalence of blue-core
column to optical fiber for electromagnetic communication is also explored and
inspires a novel application of helicon plasma, which may be one of the most
interesting findings of present work.


title:
Multi-scale and Cross-scale Contrastive Learning for Semantic
  Segmentation

abstract:
  This work considers supervised contrastive learning for semantic
segmentation. Our approach is model agnostic. We apply contrastive learning to
enhance the discriminative power of the multi-scale features extracted by
semantic segmentation networks. Our key methodological insight is to leverage
samples from the feature spaces emanating from multiple stages of a model's
encoder itself requiring neither data augmentation nor online memory banks to
obtain a diverse set of samples. To allow for such an extension we introduce an
efficient and effective sampling process, that enables applying contrastive
losses over the encoder's features at multiple scales. Furthermore, by first
mapping the encoder's multi-scale representations to a common feature space, we
instantiate a novel form of supervised local-global constraint by introducing
cross-scale contrastive learning linking high-resolution local features to
low-resolution global features. Combined, our multi-scale and cross-scale
contrastive losses boost performance of various models (DeepLabV3, HRNet,
OCRNet, UPerNet) with both CNN and Transformer backbones, when evaluated on 4
diverse datasets from natural (Cityscapes, PascalContext, ADE20K) but also
surgical (CaDIS) domains.


title:
Qualitative neural network approximation over R and C: Elementary proofs
  for analytic and polynomial activation

abstract:
  In this article, we prove approximation theorems in classes of deep and
shallow neural networks with analytic activation functions by elementary
arguments. We prove for both real and complex networks with non-polynomial
activation that the closure of the class of neural networks coincides with the
closure of the space of polynomials. The closure can further be characterized
by the Stone-Weierstrass theorem (in the real case) and Mergelyan's theorem (in
the complex case). In the real case, we further prove approximation results for
networks with higher-dimensional harmonic activation and orthogonally projected
linear maps. We further show that fully connected and residual networks of
large depth with polynomial activation functions can approximate any polynomial
under certain width requirements. All proofs are entirely elementary.


title:
Reshaping Robot Trajectories Using Natural Language Commands: A Study of
  Multi-Modal Data Alignment Using Transformers

abstract:
  Natural language is the most intuitive medium for us to interact with other
people when expressing commands and instructions. However, using language is
seldom an easy task when humans need to express their intent towards robots,
since most of the current language interfaces require rigid templates with a
static set of action targets and commands. In this work, we provide a flexible
language-based interface for human-robot collaboration, which allows a user to
reshape existing trajectories for an autonomous agent. We take advantage of
recent advancements in the field of large language models (BERT and CLIP) to
encode the user command, and then combine these features with trajectory
information using multi-modal attention transformers. We train the model using
imitation learning over a dataset containing robot trajectories modified by
language commands, and treat the trajectory generation process as a sequence
prediction problem, analogously to how language generation architectures
operate. We evaluate the system in multiple simulated trajectory scenarios, and
show a significant performance increase of our model over baseline approaches.
In addition, our real-world experiments with a robot arm show that users
significantly prefer our natural language interface over traditional methods
such as kinesthetic teaching or cost-function programming. Our study shows how
the field of robotics can take advantage of large pre-trained language models
towards creating more intuitive interfaces between robots and machines. Project
webpage: https://arthurfenderbucker.github.io/NL_trajectory_reshaper/


title:
Self-Supervised Predictive Learning: A Negative-Free Method for Sound
  Source Localization in Visual Scenes

abstract:
  Sound source localization in visual scenes aims to localize objects emitting
the sound in a given image. Recent works showing impressive localization
performance typically rely on the contrastive learning framework. However, the
random sampling of negatives, as commonly adopted in these methods, can result
in misalignment between audio and visual features and thus inducing ambiguity
in localization. In this paper, instead of following previous literature, we
propose Self-Supervised Predictive Learning (SSPL), a negative-free method for
sound localization via explicit positive mining. Specifically, we first devise
a three-stream network to elegantly associate sound source with two augmented
views of one corresponding video frame, leading to semantically coherent
similarities between audio and visual features. Second, we introduce a novel
predictive coding module for audio-visual feature alignment. Such a module
assists SSPL to focus on target objects in a progressive manner and effectively
lowers the positive-pair learning difficulty. Experiments show surprising
results that SSPL outperforms the state-of-the-art approach on two standard
sound localization benchmarks. In particular, SSPL achieves significant
improvements of 8.6% cIoU and 3.4% AUC on SoundNet-Flickr compared to the
previous best. Code is available at: https://github.com/zjsong/SSPL.


title: Finite presentations for the balanced superelliptic mapping class groups

abstract:
  The balanced superelliptic mapping class group is the normalizer of the
transformation group of the balanced superelliptic covering space in the
mapping class group of the total surface. We give finite presentations for the
balanced superelliptic mapping class groups of closed surfaces, surfaces with
one marked point, and surfaces with one boundary component. To give these
presentations, we construct finite presentations for corresponding liftable
mapping class groups in a different generating set from Ghaswala-Winarski's
presentation in~\cite{Ghaswala-Winarski1}.


title: Revisit NGC 5466 Tidal Stream with $Gaia$, SDSS/SEGUE and LAMOST

abstract:
  By mining the data from $Gaia$ EDR3, SDSS/SEGUE DR16 and LAMOST DR8, 11
member stars of the NGC 5466 tidal stream are detected and 7 of them are newly
identified. To reject contaminators, a variety of cuts are applied in sky
position, color-magnitude diagram, metallicity, proper motion and radial
velocity. We compare our data to a mock stream generated by modeling the
cluster's disruption under a smooth Galactic potential plus the Large
Magellanic Cloud (LMC). The concordant trends in phase-space between the model
and observations imply that the stream might have been perturbed by LMC. The
two most distant stars among 11 detected members trace the stream's length to
$60^\circ$ of sky, supporting and extending the previous length of $45^\circ$.
Given that NGC 5466 is so distant and potentially has a longer tail than
previously thought, we expect that NGC 5466 tidal stream could be a useful tool
in constraining the Milky Way gravitational field.


title:
The maximum $A_{\alpha}$-spectral radius of $t$-connected graphs with
  bounded matching number

abstract:
  Let $G$ be a graph with adjacency matrix $A(G)$ and let $D(G)$ be a diagonal
matrix of the degrees of $G$. In 2017, Nikiforov defined the
$A_{\alpha}$-matrix of $G$ as \begin{equation*} A_{\alpha}(G)=\alpha
G)+(1-\alpha)A(G), \end{equation*}d where $\alpha\in[0,1]$ is an arbitrary real
number. The largest eigenvalue of $A_{\alpha}(G)$ is called the
$A_{\alpha}$-spectral radius of $G$. Let $n$, $t$, $k$ be positive integers,
satisfying $t\geq1$, $k\geq2$, $n\geq k+2$, and $n\equiv k$ (mod $2$). In this
paper, for $\alpha\in[0,\frac{1}{2}]$, we determine the extremal graphs with
the maximum $A_{\alpha}$-spectral radius among all $t$-connected graphs on $n$
vertices with matching number $\frac{n-k}{2}$ at most. This generalizes some
results of O (2021) and Zhang (2022).


title:
Strange particle production in jets and underlying events in pp
  collisions at $\sqrt{s}~=~7$ TeV with PYTHIA8 generator

abstract:
  Strange hadron production in pp collisions at $\sqrt{s}~=~7$ TeV is studied
in jets and underlying events using the PYTHIA8 event generator. Matching
strange hadrons to the jet area and the underlying event area is expected to
help us disentangle the strange particles produced in hard and soft processes.
The yield and the relative production of strange hadrons dependent on the event
multiplicity are investigated with the color reconnection and color rope
mechanisms implemented in the PYTHIA8 framework. It is found that the inclusive
strange hadron productions can be reasonably described by the color
reconnection and color rope combined effects. A significant multiplicity
dependent enhancement of the strange baryon production in the jet area is
observed induced by the modified string fragmentation mechanisms, indicating
the strange baryon enhancement persists in both the hard and the soft process.
Multi-strange baryons are found to be more collimated with the jet axis than
other strange hadrons in the string fragmentation picture with the jet shape
analysis technique. Future experimental examination of these jet related
strange hadron productions will provide more insight to the origin of
strangeness enhancement in small systems.


title:
Amortized Projection Optimization for Sliced Wasserstein Generative
  Models

abstract:
  Seeking informative projecting directions has been an important task in
utilizing sliced Wasserstein distance in applications. However, finding these
directions usually requires an iterative optimization procedure over the space
of projecting directions, which is computationally expensive. Moreover, the
computational issue is even more severe in deep learning applications, where
computing the distance between two mini-batch probability measures is repeated
several times. This nested-loop has been one of the main challenges that
prevent the usage of sliced Wasserstein distances based on good projections in
practice. To address this challenge, we propose to utilize the
learning-to-optimize technique or amortized optimization to predict the
informative direction of any given two mini-batch probability measures. To the
best of our knowledge, this is the first work that bridges amortized
optimization and sliced Wasserstein generative models. In particular, we derive
linear amortized models, generalized linear amortized models, and non-linear
amortized models which are corresponding to three types of novel mini-batch
losses, named amortized sliced Wasserstein. We demonstrate the favorable
performance of the proposed sliced losses in deep generative modeling on
standard benchmark datasets.


title:
General Chaotic Behaviors of Heavy ion Collisions at Intermediate Energy
  Based on Dynamical Transport Model

abstract:
  Motivated to explore the dynamical mechanism of multi-fragmentation phase
transition and expand our knowledge on the deterministic chaos in nonlinear
dynamic process, we study systematically the nonlinear dynamical behaviors of
heavy ion collisions at intermediate energy. In order to highlight the general
characteristics of the collision dynamics, in the present paper, we simulate a
simple collision system, $Ca+Ca$, and obverse the performances of the typical
nonlinear characteristic quantities such as the production of generalized
entropy and the fractal structure of the semiclassical phase space during the
reaction process. The multifragmentation entropy, information dimension and the
dynamical fluctuations of fragment mass distribution in the final state of the
reaction are also evaluated. Our results about the intermediate processes and
the final products of the reaction are verified mutually so that confirm
strongly the existence of nonlinear chaos in the heavy ion collisions at
intermediate energy. Meanwhile, we find that the typical characteristics of the
critical phase transition obtained in the analysis of the final production of
the reaction are relatively more clear in the vicinity of the incident energies
$E=100 MeV$.


title:
Multi-wavelength Campaign on the Super-Eddington NLS1 RX J0134.2-4258 --
  I. Peculiar X-ray Spectra and Variability

abstract:
  We have conducted a new long-term multi-wavelength campaign on one of the
most super- Eddington narrow-line Seyfert 1s (NLS1s) known, namely RX
J0134.2-4258. In this first paper, we report deep simultaneous X-ray
observations performed by XMM-Newton and NuSTAR in 2019-12-19, during which RX
J0134.2-4258 was fortuitously at one of its lowest X-ray flux states. However,
there is a clear rise above 4 keV which implies that the intrinsic source flux
may be higher. The X-ray spectra observed between 1996 and 2019 show drastic
variability, probably due to complex, variable absorption along the line of
sight. Unusually, the soft X-ray excess appears extremely weak in all these
spectra, even when the hard X-ray spectrum has a steep slope of $\Gamma \simeq
2.2$. We explore the spectral-timing properties of the new (low X-ray flux) and
archival (high X-ray flux) XMM-Newton data, fitting their time-average, rms and
lag spectra simultaneously. The variability spectra indicate the presence of a
very weak soft X-ray Comptonisation component, whose shape is similar to the
soft excess in normal super-Eddington NLS1s, but with flux relative to the
power law which is lower by more than one order of magnitude. Above 4 keV the
low-flux data are dominated by a different component, which lags with respect
to the lower energy emission. This is consistent with an origin of reflection
or partial covering absorption from low ionization material located within 100
Rg. We interpret this as a further indication of the presence of a clumpy disc
wind.


title: Automatic Song Translation for Tonal Languages

abstract:
  This paper develops automatic song translation (AST) for tonal languages and
addresses the unique challenge of aligning words' tones with melody of a song
in addition to conveying the original meaning. We propose three criteria for
effective AST -- preserving meaning, singability and intelligibility -- and
design metrics for these criteria. We develop a new benchmark for
English--Mandarin song translation and develop an unsupervised AST system,
Guided AliGnment for Automatic Song Translation (GagaST), which combines
pre-training with three decoding constraints. Both automatic and human
evaluations show GagaST successfully balances semantics and singability.


title: Learning Losses for Strategic Classification

abstract:
  Strategic classification, i.e. classification under possible strategic
manipulations of features, has received a lot of attention from both the
machine learning and the game theory community. Most works focus on analysing
properties of the optimal decision rule under such manipulations. In our work
we take a learning theoretic perspective, focusing on the sample complexity
needed to learn a good decision rule which is robust to strategic manipulation.
We perform this analysis by introducing a novel loss function, the
\emph{strategic manipulation loss}, which takes into account both the accuracy
of the final decision rule and its vulnerability to manipulation. We analyse
the sample complexity for a known graph of possible manipulations in terms of
the complexity of the function class and the manipulation graph. Additionally,
we initialize the study of learning under unknown manipulation capabilities of
the involved agents. Using techniques from transfer learning theory, we define
a similarity measure for manipulation graphs and show that learning outcomes
are robust with respect to small changes in the manipulation graph. Lastly, we
analyse the (sample complexity of) learning of the manipulation capability of
agents with respect to this similarity measure, providing novel guarantees for
strategic classification with respect to an unknown manipulation graph.


title:
Pseudo-Label Transfer from Frame-Level to Note-Level in a
  Teacher-Student Framework for Singing Transcription from Polyphonic Music

abstract:
  Lack of large-scale note-level labeled data is the major obstacle to singing
transcription from polyphonic music. We address the issue by using pseudo
labels from vocal pitch estimation models given unlabeled data. The proposed
method first converts the frame-level pseudo labels to note-level through pitch
and rhythm quantization steps. Then, it further improves the label quality
through self-training in a teacher-student framework. To validate the method,
we conduct various experiment settings by investigating two vocal pitch
estimation models as pseudo-label generators, two setups of teacher-student
frameworks, and the number of iterations in self-training. The results show
that the proposed method can effectively leverage large-scale unlabeled audio
data and self-training with the noisy student model helps to improve
performance. Finally, we show that the model trained with only unlabeled data
has comparable performance to previous works and the model trained with
additional labeled data achieves higher accuracy than the model trained with
only labeled data.


title: Modeling Attrition in Recommender Systems with Departing Bandits

abstract:
  Traditionally, when recommender systems are formalized as multi-armed
bandits, the policy of the recommender system influences the rewards accrued,
but not the length of interaction. However, in real-world systems, dissatisfied
users may depart (and never come back). In this work, we propose a novel
multi-armed bandit setup that captures such policy-dependent horizons. Our
setup consists of a finite set of user types, and multiple arms with Bernoulli
payoffs. Each (user type, arm) tuple corresponds to an (unknown) reward
probability. Each user's type is initially unknown and can only be inferred
through their response to recommendations. Moreover, if a user is dissatisfied
with their recommendation, they might depart the system. We first address the
case where all users share the same type, demonstrating that a recent UCB-based
algorithm is optimal. We then move forward to the more challenging case, where
users are divided among two types. While naive approaches cannot handle this
setting, we provide an efficient learning algorithm that achieves
$\tilde{O}(\sqrt{T})$ regret, where $T$ is the number of users.


title: Dealing with Sparse Rewards Using Graph Neural Networks

abstract:
  Deep reinforcement learning in partially observable environments is a
difficult task in itself, and can be further complicated by a sparse reward
signal. Most tasks involving navigation in three-dimensional environments
provide the agent with extremely limited information. Typically, the agent
receives a visual observation input from the environment and is rewarded once
at the end of the episode. A good reward function could substantially improve
the convergence of reinforcement learning algorithms for such tasks. The
classic approach to increase the density of the reward signal is to augment it
with supplementary rewards. This technique is called the reward shaping. In
this study, we propose two modifications of one of the recent reward shaping
methods based on graph convolutional networks: the first involving advanced
aggregation functions, and the second utilizing the attention mechanism. We
empirically validate the effectiveness of our solutions for the task of
navigation in a 3D environment with sparse rewards. For the solution featuring
attention mechanism, we are also able to show that the learned attention is
concentrated on edges corresponding to important transitions in 3D environment.


title: Scattering-asymmetry control with ultrafast electron wave packet shaping

abstract:
  Scattering of a tightly focused electron beam by an atom forms one of the
bases of modern electron microscopy. A fundamental symmetry breaking occurs
when the target atom is displaced from the beam center. This displacement
results in an asymmetry in the angular distribution of the scattered electrons.
Here we propose a concept to control the sign and magnitude of the asymmetry by
shaping the incident high-energy electron wave packet in momentum space on the
atto- to picosecond time scale. The shaping controls the ultrafast real-space
dynamics of the wave packet, shifting the balance between two competing
contributions of the impact-parameter-dependent quantum interference and the
momentum distribution of the wave packet on the target. We find a strong
sensitivity of the scattering on the wave packet properties, an effect that
opens promising new avenues for ultrafast electron microscopy.


title:
Reconciling monolayer and bilayer $J_{\rm eff} = 1/2$ square lattices in
  hybrid oxide superlattice

abstract:
  The number of atomic layers confined in a two-dimensional structure is
crucial for the electronic and magnetic properties. Single-layer and bilayer
$J_{\rm eff} = 1/2$ square lattices are well-known examples where the presence
of the extra layer turns the XY-anisotropy to the $c$-axis anisotropy. We
report on experimental realization of a hybrid SrIrO$_3$/SrTiO$_3$ superlattice
that integrates monolayer and bilayer square lattices in one layered structure.
By synchrotron x-ray diffraction, resonant x-ray magnetic scattering,
magnetization, and resistivity measurements, we found that the hybrid
superlattice exhibits properties that are distinct from both the single-layer
and bilayer systems and cannot be explained by a simple addition of them. In
particular, the entire hybrid superlattice orders simultaneously through a
single antiferromagnetic transition at temperatures similar to the bilayer
system but with all the $J_{\rm eff} = 1/2$ moments mainly pointing in the
$ab$-plane similar to the single-layer system. The results show that bringing
monolayer and bilayer with orthogonal properties in proximity to each other in
a hybrid superlattice structure is a powerful way to stabilize a unique state
not obtainable in a uniform structure.


title:
Noisy Boundaries: Lemon or Lemonade for Semi-supervised Instance
  Segmentation?

abstract:
  Current instance segmentation methods rely heavily on pixel-level annotated
images. The huge cost to obtain such fully-annotated images restricts the
dataset scale and limits the performance. In this paper, we formally address
semi-supervised instance segmentation, where unlabeled images are employed to
boost the performance. We construct a framework for semi-supervised instance
segmentation by assigning pixel-level pseudo labels. Under this framework, we
point out that noisy boundaries associated with pseudo labels are double-edged.
We propose to exploit and resist them in a unified manner simultaneously: 1) To
combat the negative effects of noisy boundaries, we propose a noise-tolerant
mask head by leveraging low-resolution features. 2) To enhance the positive
impacts, we introduce a boundary-preserving map for learning detailed
information within boundary-relevant regions. We evaluate our approach by
extensive experiments. It behaves extraordinarily, outperforming the supervised
baseline by a large margin, more than 6% on Cityscapes, 7% on COCO and 4.5% on
BDD100k. On Cityscapes, our method achieves comparable performance by utilizing
only 30% labeled images.


title:
Geometric model of crack-templated networks for transparent conductive
  films

abstract:
  Crack-templated networks, metallic frameworks fabricated from crack patterns
in sacrificial thin films, can exhibit high optical transmittance, high
electric conductivity, and a host of other properties attractive for
applications. Despite advances in preparing, characterizing, and analyzing
optoelectronic performance of cracked template networks, limited efforts have
focused on predicting how their disordered structures help determine their
electrical and optical properties and explain their interrelationships. We
introduce a geometric modeling approach for crack-templated networks and use
simulation to compute their wavelength- and incident angle-dependent optical
transmittance and sheet resistivity. We explore how these properties relate to
one another and to those of metallic meshes with periodically ordered aperture
arrays. We consider implications of the results for optoelectronic
applications, compare figure-of-merit predictions to experimental data, and
highlight an opportunity to extend the modeling approach using inverse methods.


title: Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map

abstract:
  Motion planning in off-road environments requires reasoning about both the
geometry and semantics of the scene (e.g., a robot may be able to drive through
soft bushes but not a fallen log). In many recent works, the world is
classified into a finite number of semantic categories that often are not
sufficient to capture the ability (i.e., the speed) with which a robot can
traverse off-road terrain. Instead, this work proposes a new representation of
traversability based exclusively on robot speed that can be learned from data,
offers interpretability and intuitive tuning, and can be easily integrated with
a variety of planning paradigms in the form of a costmap. Specifically, given a
dataset of experienced trajectories, the proposed algorithm learns to predict a
distribution of speeds the robot could achieve, conditioned on the environment
semantics and commanded speed. The learned speed distribution map is converted
into costmaps with a risk-aware cost term based on conditional value at risk
(CVaR). Numerical simulations demonstrate that the proposed risk-aware planning
algorithm leads to faster average time-to-goals compared to a method that only
considers expected behavior, and the planner can be tuned for slightly slower,
but less variable behavior. Furthermore, the approach is integrated into a full
autonomy stack and demonstrated in a high-fidelity Unity environment and is
shown to provide a 30\% improvement in the success rate of navigation.


title:
Plagiarism Detection in the Bengali Language: A Text Similarity-Based
  Approach

abstract:
  Plagiarism means taking another person's work and not giving any credit to
them for it. Plagiarism is one of the most serious problems in academia and
among researchers. Even though there are multiple tools available to detect
plagiarism in a document but most of them are domain-specific and designed to
work in English texts, but plagiarism is not limited to a single language only.
Bengali is the most widely spoken language of Bangladesh and the second most
spoken language in India with 300 million native speakers and 37 million
second-language speakers. Plagiarism detection requires a large corpus for
comparison. Bengali Literature has a history of 1300 years. Hence most Bengali
Literature books are not yet digitalized properly. As there was no such corpus
present for our purpose so we have collected Bengali Literature books from the
National Digital Library of India and with a comprehensive methodology
extracted texts from it and constructed our corpus. Our experimental results
find out average accuracy between 72.10 % - 79.89 % in text extraction using
OCR. Levenshtein Distance algorithm is used for determining Plagiarism. We have
built a web application for end-user and successfully tested it for Plagiarism
detection in Bengali texts. In future, we aim to construct a corpus with more
books for more accurate detection.


title:
Aspect-Oriented Programming based building block platform to construct
  Domain-Specific Language for HPC application

abstract:
  The world of HPC systems is changing to a more complicated system because the
performance improvement of processors has been slowed down. One of the
promising approaches is Domain-Specific Language(DSL), which provides a
productive environment to create a high-efficient program without pain.
However, existing DSL platforms themselves often lack portability and cost DSL
developers great effort. To solve this issue, we propose an Aspect-Oriented
Programming(AOP) based DSL constructing platform, enabling developers to build
a DSL platform by combining Aspect modules. Aspect modules manage abstracted
application flow, data structure, and memory access on our platform. Therefore,
developers can create any DSL platform whose target application has the
attributes which HPC applications usually have, the abstraction assumes. This
study implemented a prototype platform that can handle MPI and OpenMP layers.
The prototype supports three types of applications (Structured-Grid,
Unstructured-Grid, and Particle Simulation). Then, we evaluated the overheads
caused by achieving flexibility and productivity of the platform.


title: Nash Neural Networks : Inferring Utilities from Optimal Behaviour

abstract:
  We propose Nash Neural Networks ($N^3$) as a new type of Physics Informed
Neural Network that is able to infer the underlying utility from observations
of how rational individuals behave in a differential game with a Nash
equilibrium. We assume that the dynamics for both the population and the
individual are known, but not the payoff function, which specifies the cost per
unit time of being in any particular state. We construct our network in such a
way that the Euler-Lagrange equations of the corresponding optimal control
problem are satisfied and the optimal control is self-consistently determined.
In this way, we are able to learn the unknown payoff function in an
unsupervised manner. We have applied the $N^3$ to study the optimal behaviour
during epidemics, in which individuals can choose to socially distance
depending on the state of the pandemic and the cost of being infected. Training
our network against synthetic data for a simple SIR model, we showed that it is
possible to accurately reproduce the hidden payoff function, in such a way that
the game dynamics are respected. Our approach will have far-reaching
applications, as it allows one to infer utilities from behavioural data, and
can thus be applied to study a wide array of problems in science, engineering,
economics and government planning.


title:
A Robust and Statistically Efficient Maximum-Likelihood Method for DOA
  Estimation Using Sparse Linear Arrays

abstract:
  A recent trend of research on direction-of-arrival (DOA) estimation is to
localize more uncorrelated sources than sensors by using a proper sparse linear
array (SLA) and the Toeplitz covariance structure, at a cost of robustness to
source correlations. In this paper, we make an attempt to achieve the two goals
simultaneously by using a single algorithm. In order to statistically
efficiently localize a maximal number of uncorrelated sources, we propose an
effective algorithm for the stochastic maximum likelihood (SML) method based on
elegant problem reformulations and the alternating direction method of
multipliers (ADMM). We prove that the SML is robust to source correlations
though it is derived under the assumption of uncorrelated sources. The proposed
algorithm is usable for arbitrary SLAs (e.g., minimum redundancy arrays, nested
arrays and coprime arrays) and is named as {\em m}aximum-likelihood {\em
e}stimation via {\em s}equential {\em A}DMM (MESA). Extensive numerical results
are provided that collaborate our analysis and demonstrate the statistical
efficiency and robustness of MESA among state-of-the-art algorithms.


title: Structural Correlates Of Spatial Navigation And Memory Formation

abstract:
  Spatial learning across many species is impaired by lesions in the
hippocampus, a subcortical brain structure whose cellular composition changes
substantially over its 5 to 6 week lifetime from mainly excitatory neurons
during development to equal proportions of inhibitory interneurons (gamma
Amp/Arcs) as well as pyramidal cells early in life, but which later on
comprises only about 10% Arches+ projection spiny cortical projecting principal
cells that are located within discrete cytoarchitectonic patches known as CA3
or just hilus granular layer 2 (CG2). While other structures may contribute
importantly in certain situations e.g., perirhinal cortex when using visual
cues with no reference frame for location), these remaining cell types also
change their proportion through time with gamma APs forming 30 to 35 percent),
fast firing parvalbumin immunoreactive basket or axoaxonic synapses into
somatic spines at 40 days after birth (DABs formed into spine membranes by 15
DAB), and CG2 pyramids forming relatively slowly but strengthening synapses
onto nearby dendritic shafts made up largely (>80 %) by GABAergic terminals
(+spines) until then that were still synaptically silent.


title: Independent set reconfiguration on directed graphs

abstract:
  \textsc{Directed Token Sliding} asks, given a directed graph and two sets of
pairwise nonadjacent vertices, whether one can reach from one set to the other
by repeatedly applying a local operation that exchanges a vertex in the current
set with one of its out-neighbors, while keeping the nonadjacency. It can be
seen as a reconfiguration process where a token is placed on each vertex in the
current set, and the local operation slides a token along an arc respecting its
direction. Previously, such a problem was extensively studied on undirected
graphs, where the edges have no directions and thus the local operation is
symmetric. \textsc{Directed Token Sliding} is a generalization of its
undirected variant since an undirected edge can be simulated by two arcs of
opposite directions.
  In this paper, we initiate the algorithmic study of \textsc{Directed Token
Sliding}. We first observe that the problem is PSPACE-complete even if we
forbid parallel arcs in opposite directions and that the problem on directed
acyclic graphs is NP-complete and W[1]-hard parameterized by the size of the
sets in consideration. We then show our main result: a linear-time algorithm
for the problem on directed graphs whose underlying undirected graphs are
trees, which are called polytrees. Such a result is also known for the
undirected variant of the problem on trees~[Demaine et al.~TCS 2015], but the
techniques used here are quite different because of the asymmetric nature of
the directed problem. We present a characterization of yes-instances based on
the existence of a certain set of directed paths, and then derive simple
equivalent conditions from it by some observations, which admits an efficient
algorithm. For the polytree case, we also present a quadratic-time algorithm
that outputs, if the input is a yes-instance, one of the shortest
reconfiguration sequences.


title:
Frame-level Prediction of Facial Expressions, Valence, Arousal and
  Action Units for Mobile Devices

abstract:
  In this paper, we consider the problem of real-time video-based facial
emotion analytics, namely, facial expression recognition, prediction of valence
and arousal and detection of action unit points. We propose the novel
frame-level emotion recognition algorithm by extracting facial features with
the single EfficientNet model pre-trained on AffectNet. As a result, our
approach may be implemented even for video analytics on mobile devices.
Experimental results for the large scale Aff-Wild2 database from the third
Affective Behavior Analysis in-the-wild (ABAW) Competition demonstrate that our
simple model is significantly better when compared to the VggFace baseline. In
particular, our method is characterized by 0.15-0.2 higher performance measures
for validation sets in uni-task Expression Classification, Valence-Arousal
Estimation and Expression Classification. Due to simplicity, our approach may
be considered as a new baseline for all four sub-challenges.


title: BCOT: A Markerless High-Precision 3D Object Tracking Benchmark

abstract:
  Template-based 3D object tracking still lacks a high-precision benchmark of
real scenes due to the difficulty of annotating the accurate 3D poses of real
moving video objects without using markers. In this paper, we present a
multi-view approach to estimate the accurate 3D poses of real moving objects,
and then use binocular data to construct a new benchmark for monocular
textureless 3D object tracking. The proposed method requires no markers, and
the cameras only need to be synchronous, relatively fixed as cross-view and
calibrated. Based on our object-centered model, we jointly optimize the object
pose by minimizing shape re-projection constraints in all views, which greatly
improves the accuracy compared with the single-view approach, and is even more
accurate than the depth-based method. Our new benchmark dataset contains 20
textureless objects, 22 scenes, 404 video sequences and 126K images captured in
real scenes. The annotation error is guaranteed to be less than 2mm, according
to both theoretical analysis and validation experiments. We re-evaluate the
state-of-the-art 3D object tracking methods with our dataset, reporting their
performance ranking in real scenes. Our BCOT benchmark and code can be found at
https://ar3dv.github.io/BCOT-Benchmark/.


title:
Microstructure Surface Reconstruction from SEM Images: An Alternative to
  Digital Image Correlation (DIC)

abstract:
  We reconstruct a 3D model of the surface of a material undergoing fatigue
testing and experiencing cracking. Specifically we reconstruct the surface
depth (out of plane intrusions and extrusions) and lateral (in-plane) motion
from multiple views of the sample at the end of the experiment, combined with a
reverse optical flow propagation backwards in time that utilizes interim single
view images. These measurements can be mapped to a material strain tensor which
helps to understand material life and predict failure. This approach offers an
alternative to the commonly used Digital Image Correlation (DIC) technique
which relies on tracking a speckle pattern applied to the material surface. DIC
only produces in-plane (2D) measurements whereas our approach is 3D and
non-invasive (requires no pattern being applied to the material).


title:
On removable singularities for solutions of Neumann problem for elliptic
  equations involving variable exponent

abstract:
  We study the removability of a singular set in the boundary of Neumann
problem for elliptic equations with variable exponent. We consider the case
when the singular set has a smooth structure, and give sufficient conditions
for removability of this singularity for the equation in the variable exponent
Sobolev space.


title: Broken family sensitivity in transitive systems

abstract:
  Let $(X,T)$ be a topological dynamical system, $n\geq 2$ and $\mathcal{F}$ be
a Furstenberg family of subsets of $\mathbb{Z}_+$. $(X,T)$ is called broken
$\mathcal{F}$-$n$-sensitive if there exist $\delta>0$ and $F\in\mathcal{F}$
such that for every opene (non-empty open) subset $U$ of $X$ and every
$l\in\mathbb{N}$, there exist $x_1^l,x_2^l,\dotsc,x_n^l\in U$ and $m_l\in
\mathbb{Z}_+$ satisfying $d(T^k x_i^l, T^k x_j^l)> \delta,\ \forall 1\leq
i<j\leq n, k\in m_l+ F\cap[1,l]$. We investigate broken
$\mathcal{F}$-$n$-sensitivity for the family of all piecewise syndetic subsets
($\mathcal{F}_{ps}$), the family of all positive upper Banach density subsets
($\mathcal{F}_{pubd}$) and the family of all infinite subsets
($\mathcal{F}_{inf}$). We show that a transitive system $(X,T)$ is broken
$\mathcal{F}$-$n$-sensitive for $\mathcal{F}=\mathcal{F}_{ps}\ \text{or}\
\mathcal{F}_{pubd}$ if and only if there exists an essential $n$-sensitive
tuple which is an $\mathcal{F}$-recurrent point of $(X^n, T^{(n)})$; is broken
$\mathcal{F}_{inf}$-$n$-sensitive if and only if there exists an essential
$n$-sensitive tuple $(x_1,x_2,\dotsc,x_n)$ such that
$\limsup_{k\to\infty}\min_{1\leq i<j\leq n}d(T^kx_i,T^kx_j)>0$. We also obtain
specific properties for them by analyzing the factor maps to their maximal
equicontinuous factors. Furthermore, we show examples to distinguish different
kinds of broken family sensitivity.


title: 3D GAN Inversion for Controllable Portrait Image Animation

abstract:
  Millions of images of human faces are captured every single day; but these
photographs portray the likeness of an individual with a fixed pose,
expression, and appearance. Portrait image animation enables the post-capture
adjustment of these attributes from a single image while maintaining a
photorealistic reconstruction of the subject's likeness or identity. Still,
current methods for portrait image animation are typically based on 2D warping
operations or manipulations of a 2D generative adversarial network (GAN) and
lack explicit mechanisms to enforce multi-view consistency. Thus these methods
may significantly alter the identity of the subject, especially when the
viewpoint relative to the camera is changed. In this work, we leverage newly
developed 3D GANs, which allow explicit control over the pose of the image
subject with multi-view consistency. We propose a supervision strategy to
flexibly manipulate expressions with 3D morphable models, and we show that the
proposed method also supports editing appearance attributes, such as age or
hairstyle, by interpolating within the latent space of the GAN. The proposed
technique for portrait image animation outperforms previous methods in terms of
image quality, identity preservation, and pose transfer while also supporting
attribute editing.


title:
Dualband and Tripleband Metamaterial absorber in WR1 band and Lower
  TeraHertz frequencies

abstract:
  Frequency Band of 0.5 THz to 1.1 THz (WR1.5 and WR1) is one of the promising
bands when it comes to 6G. In this paper, we propose a novel metamaterial
absorber suitable to be used at lower WR1 frequencies in TE mode.
Alternatively, the design can be used as a strong absorber at lower Terahertz
frequencies. In addition, when used in TM mode the absorber works as a perfect
absorber at higher WR1 band.


title:
MDAN: Multi-level Dependent Attention Network for Visual Emotion
  Analysis

abstract:
  Visual Emotion Analysis (VEA) is attracting increasing attention. One of the
biggest challenges of VEA is to bridge the affective gap between visual clues
in a picture and the emotion expressed by the picture. As the granularity of
emotions increases, the affective gap increases as well. Existing deep
approaches try to bridge the gap by directly learning discrimination among
emotions globally in one shot without considering the hierarchical relationship
among emotions at different affective levels and the affective level of
emotions to be classified. In this paper, we present the Multi-level Dependent
Attention Network (MDAN) with two branches, to leverage the emotion hierarchy
and the correlation between different affective levels and semantic levels. The
bottom-up branch directly learns emotions at the highest affective level and
strictly follows the emotion hierarchy while predicting emotions at lower
affective levels. In contrast, the top-down branch attempt to disentangle the
affective gap by one-to-one mapping between semantic levels and affective
levels, namely, Affective Semantic Mapping. At each semantic level, a local
classifier learns discrimination among emotions at the corresponding affective
level. Finally, We integrate global learning and local learning into a unified
deep framework and optimize the network simultaneously. Moreover, to properly
extract and leverage channel dependencies and spatial attention while
disentangling the affective gap, we carefully designed two attention modules:
the Multi-head Cross Channel Attention module and the Level-dependent Class
Activation Map module. Finally, the proposed deep framework obtains new
state-of-the-art performance on six VEA benchmarks, where it outperforms
existing state-of-the-art methods by a large margin, e.g., +3.85% on the WEBEmo
dataset at 25 classes classification accuracy.


title:
Vision Transformer Compression with Structured Pruning and Low Rank
  Approximation

abstract:
  Transformer architecture has gained popularity due to its ability to scale
with large dataset. Consequently, there is a need to reduce the model size and
latency, especially for on-device deployment. We focus on vision transformer
proposed for image recognition task (Dosovitskiy et al., 2021), and explore the
application of different compression techniques such as low rank approximation
and pruning for this purpose. Specifically, we investigate a structured pruning
method proposed recently in Zhu et al. (2021) and find that mostly feedforward
blocks are pruned with this approach, that too, with severe degradation in
accuracy. We propose a hybrid compression approach to mitigate this where we
compress the attention blocks using low rank approximation and use the
previously mentioned pruning with a lower rate for feedforward blocks in each
transformer layer. Our technique results in 50% compression with 14% relative
increase in classification error whereas we obtain 44% compression with 20%
relative increase in error when only pruning is applied. We propose further
enhancements to bridge the accuracy gap but leave it as a future work.


title: C to Checked C by 3C

abstract:
  Owing to the continued use of C (and C++), spatial safety violations (e.g.,
buffer overflows) still constitute one of today's most dangerous and prevalent
security vulnerabilities. To combat these violations, Checked C extends C with
bounds-enforced checked pointer types. Checked C is essentially a gradually
typed spatially safe C - checked pointers are backwards-binary compatible with
legacy pointers, and the language allows them to be added piecemeal, rather
than necessarily all at once, so that safety retrofitting can be incremental.
This paper presents a semi-automated process for porting a legacy C program to
Checked C. The process centers on 3C, a static analysis-based annotation tool.
3C employs two novel static analysis algorithms - typ3c and boun3c - to
annotate legacy pointers as checked pointers, and to infer array bounds
annotations for pointers that need them. 3C performs a root cause analysis to
direct a human developer to code that should be refactored; once done, 3C can
be re-run to infer further annotations (and updated root causes). Experiments
on 11 programs totaling 319KLoC show 3C to be effective at inferring checked
pointer types, and experience with previously and newly ported code finds 3C
works well when combined with human-driven refactoring.


title: Randomized Policy Optimization for Optimal Stopping

abstract:
  Optimal stopping is the problem of determining when to stop a stochastic
system in order to maximize reward, which is of practical importance in domains
such as finance, operations management and healthcare. Existing methods for
high-dimensional optimal stopping that are popular in practice produce
deterministic linear policies -- policies that deterministically stop based on
the sign of a weighted sum of basis functions -- but are not guaranteed to find
the optimal policy within this policy class given a fixed basis function
architecture. In this paper, we propose a new methodology for optimal stopping
based on randomized linear policies, which choose to stop with a probability
that is determined by a weighted sum of basis functions. We motivate these
policies by establishing that under mild conditions, given a fixed basis
function architecture, optimizing over randomized linear policies is equivalent
to optimizing over deterministic linear policies. We formulate the problem of
learning randomized linear policies from data as a smooth non-convex sample
average approximation (SAA) problem. We theoretically prove the almost sure
convergence of our randomized policy SAA problem and establish bounds on the
out-of-sample performance of randomized policies obtained from our SAA problem
based on Rademacher complexity. We also show that the SAA problem is in general
NP-Hard, and consequently develop a practical heuristic for solving our
randomized policy problem. Through numerical experiments on a benchmark family
of option pricing problem instances, we show that our approach can
substantially outperform state-of-the-art methods.


title:
Component-wise Analysis of Automatically Designed Multiobjective
  Algorithms on Constrained Problems

abstract:
  The performance of multiobjective algorithms varies across problems, making
it hard to develop new algorithms or apply existing ones to new problems. To
simplify the development and application of new multiobjective algorithms,
there has been an increasing interest in their automatic design from component
parts. These automatically designed metaheuristics can outperform their
human-developed counterparts. However, it is still uncertain what are the most
influential components leading to their performance improvement. This study
introduces a new methodology to investigate the effects of the final
configuration of an automatically designed algorithm. We apply this methodology
to a well-performing Multiobjective Evolutionary Algorithm Based on
Decomposition (MOEA/D) designed by the irace package on nine constrained
problems. We then contrast the impact of the algorithm components in terms of
their Search Trajectory Networks (STNs), the diversity of the population, and
the hypervolume. Our results indicate that the most influential components were
the restart and update strategies, with higher increments in performance and
more distinct metric values. Also, their relative influence depends on the
problem difficulty: not using the restart strategy was more influential in
problems where MOEA/D performs better; while the update strategy was more
influential in problems where MOEA/D performs the worst.


title:
AudioTagging Done Right: 2nd comparison of deep learning methods for
  environmental sound classification

abstract:
  After its sweeping success in vision and language tasks, pure attention-based
neural architectures (e.g. DeiT) are emerging to the top of audio tagging (AT)
leaderboards, which seemingly obsoletes traditional convolutional neural
networks (CNNs), feed-forward networks or recurrent networks. However, taking a
closer look, there is great variability in published research, for instance,
performances of models initialized with pretrained weights differ drastically
from without pretraining, training time for a model varies from hours to weeks,
and often, essences are hidden in seemingly trivial details. This urgently
calls for a comprehensive study since our 1st comparison is half-decade old. In
this work, we perform extensive experiments on AudioSet which is the largest
weakly-labeled sound event dataset available, we also did an analysis based on
the data quality and efficiency. We compare a few state-of-the-art baselines on
the AT task, and study the performance and efficiency of 2 major categories of
neural architectures: CNN variants and attention-based variants. We also
closely examine their optimization procedures. Our opensourced experimental
results provide insights to trade-off between performance, efficiency,
optimization process, for both practitioners and researchers. Implementation:
https://github.com/lijuncheng16/AudioTaggingDoneRight


title:
A Comparative Evaluation of Machine Learning Algorithms for the
  Prediction of R/C Buildings' Seismic Damage

abstract:
  Seismic assessment of buildings and determination of their structural damage
is at the forefront of modern scientific research. Since now, several
researchers have proposed a number of procedures, in an attempt to estimate the
damage response of the buildings subjected to strong ground motions, without
conducting time-consuming analyses. These procedures, e.g. construction of
fragility curves, usually utilize methods based on the application of
statistical theory. In the last decades, the increase of the computers' power
has led to the development of modern soft computing methods based on the
adoption of Machine Learning algorithms. The present paper attempts an
extensive comparative evaluation of the capability of various Machine Learning
methods to adequately predict the seismic response of R/C buildings. The
training dataset is created by means of Nonlinear Time History Analyses of 90
3D R/C buildings with three different masonry infills' distributions, which are
subjected to 65 earthquakes. The seismic damage is expressed in terms of the
Maximum Interstory Drift Ratio. A large-scale comparison study is utilized by
the most efficient Machine Learning algorithms. The experimentation shows that
the LightGBM approach produces training stability, high overall performance and
a remarkable coefficient of determination to estimate the ability to predict
the buildings' damage response. Due to the extremely urgent issue, civil
protection mechanisms need to incorporate in their technological systems
scientific methodologies and appropriate technical or modeling tools such as
the proposed one, which can offer valuable assistance in making optimal
decisions.


title: A Comparative Survey of Deep Active Learning

abstract:
  Active Learning (AL) is a set of techniques for reducing labeling cost by
sequentially selecting data samples from a large unlabeled data pool for
labeling. Meanwhile, Deep Learning (DL) is data-hungry, and the performance of
DL models scales monotonically with more training data. Therefore, in recent
years, Deep Active Learning (DAL) has risen as feasible solutions for
maximizing model performance while minimizing the expensive labeling cost.
Abundant methods have sprung up and literature reviews of DAL have been
presented before. However, the performance comparison of different branches of
DAL methods under various tasks is still insufficient and our work fills this
gap. In this paper, we survey and categorize DAL-related work and construct
comparative experiments across frequently used datasets and DAL algorithms.
Additionally, we explore some factors (e.g., batch size, number of epochs in
the training process) that influence the efficacy of DAL, which provides better
references for researchers to design their own DAL experiments or carry out
DAL-related applications. We construct a DAL toolkit, DeepAL+, by
re-implementing many highly-cited DAL-related methods, and it will be released
to the public.


title: Quantum dynamics is not strictly bidivisible

abstract:
  We address the question of the existence of quantum channels that are
divisible in two quantum channels but not in three, or more generally channels
divisible in $n$ but not in $n+1$ parts. We show that for the qubit, those
channels \textit{do not} exist, whereas for general finite-dimensional quantum
channels the same holds at least for full Kraus rank channels. To prove these
results we introduce a novel decomposition of quantum channels which separates
them in a boundary and Markovian part, and it holds for any finite dimension.
Additionally, the introduced decomposition amounts to the well known connection
between divisibility classes and implementation types of quantum dynamical
maps.


title:
PCA-Based Knowledge Distillation Towards Lightweight and Content-Style
  Balanced Photorealistic Style Transfer Models

abstract:
  Photorealistic style transfer entails transferring the style of a reference
image to another image so the result seems like a plausible photo. Our work is
inspired by the observation that existing models are slow due to their large
sizes. We introduce PCA-based knowledge distillation to distill lightweight
models and show it is motivated by theory. To our knowledge, this is the first
knowledge distillation method for photorealistic style transfer. Our
experiments demonstrate its versatility for use with different backbone
architectures, VGG and MobileNet, across six image resolutions. Compared to
existing models, our top-performing model runs at speeds 5-20x faster using at
most 1\% of the parameters. Additionally, our distilled models achieve a better
balance between stylization strength and content preservation than existing
models. To support reproducing our method and models, we share the code at
\textit{https://github.com/chiutaiyin/PCA-Knowledge-Distillation}.


title: CNN LEGO: Disassembling and Assembling Convolutional Neural Network

abstract:
  Convolutional Neural Network (CNN), which mimics human visual perception
mechanism, has been successfully used in many computer vision areas. Some
psychophysical studies show that the visual perception mechanism synchronously
processes the form, color, movement, depth, etc., in the initial stage [7,20]
and then integrates all information for final recognition [38]. What's more,
the human visual system [20] contains different subdivisions or different
tasks. Inspired by the above visual perception mechanism, we investigate a new
task, termed as Model Disassembling and Assembling (MDA-Task), which can
disassemble the deep models into independent parts and assemble those parts
into a new deep model without performance cost like playing LEGO toys. To this
end, we propose a feature route attribution technique (FRAT) for disassembling
CNN classifiers in this paper. In FRAT, the positive derivatives of predicted
class probability w.r.t. the feature maps are adopted to locate the critical
features in each layer. Then, relevance analysis between the critical features
and preceding/subsequent parameter layers is adopted to bridge the route
between two adjacent parameter layers. In the assembling phase, class-wise
components of each layer are assembled into a new deep model for a specific
task. Extensive experiments demonstrate that the assembled CNN classifier can
achieve close accuracy with the original classifier without any fine-tune, and
excess original performance with one-epoch fine-tune. What's more, we also
conduct massive experiments to verify the broad application of MDA-Task on
model decision route visualization, model compression, knowledge distillation,
transfer learning, incremental learning, and so on.


title: Measuring CP violation in rare $W$ decays at the LHC

abstract:
  Heavy Majorana neutrinos beyond the standard model can simultaneously explain
the origin of tiny neutrino masses and matter-antimatter asymmetry in our
Universe. The existence of heavy Majorana neutrinos will also lead to lepton
number violation and the rare lepton-number-violating $W$ decays are possible.
With contributions from two different Majorana neutrinos, a nonzero CP
asymmetry may be generated from the rate difference between $W$ decay and its
CP-conjugate process. The aim of this paper is to investigate the prospects for
measuring CP violation in rare $W$ decays via Majorana neutrinos at the LHC.
Our calculations show that the induced CP asymmetry is independent of the
Majorana neutrino mass for $10~ \rm GeV < m_N < 70 ~\rm GeV$. Such a CP
asymmetry if observed, would in turn provide unambiguous evidence of new
physics beyond the standard model.


title:
A Unified Contrastive Energy-based Model for Understanding the
  Generative Ability of Adversarial Training

abstract:
  Adversarial Training (AT) is known as an effective approach to enhance the
robustness of deep neural networks. Recently researchers notice that robust
models with AT have good generative ability and can synthesize realistic
images, while the reason behind it is yet under-explored. In this paper, we
demystify this phenomenon by developing a unified probabilistic framework,
called Contrastive Energy-based Models (CEM). On the one hand, we provide the
first probabilistic characterization of AT through a unified understanding of
robustness and generative ability. On the other hand, our unified framework can
be extended to the unsupervised scenario, which interprets unsupervised
contrastive learning as an important sampling of CEM. Based on these, we
propose a principled method to develop adversarial learning and sampling
methods. Experiments show that the sampling methods derived from our framework
improve the sample quality in both supervised and unsupervised learning.
Notably, our unsupervised adversarial sampling method achieves an Inception
score of 9.61 on CIFAR-10, which is superior to previous energy-based models
and comparable to state-of-the-art generative models.


title:
Non-uniqueness in law of transport-diffusion equation forced by random
  noise

abstract:
  We consider a transport-diffusion equation forced by random noise of three
types: additive, linear multiplicative in It$\hat{\mathrm{o}}$'s
interpretation, and transport in Stratonovich's interpretation. Via convex
integration modified to probabilistic setting, we prove existence of a
divergence-free vector field with spatial regularity in Sobolev space and
corresponding solution to a transport-diffusion equation with spatial
regularity in Lebesgue space, and consequently non-uniqueness in law at the
level of probabilistically strong solutions globally in time.


title:
Chaos is a Ladder: A New Theoretical Understanding of Contrastive
  Learning via Augmentation Overlap

abstract:
  Recently, contrastive learning has risen to be a promising approach for
large-scale self-supervised learning. However, theoretical understanding of how
it works is still unclear. In this paper, we propose a new guarantee on the
downstream performance without resorting to the conditional independence
assumption that is widely adopted in previous work but hardly holds in
practice. Our new theory hinges on the insight that the support of different
intra-class samples will become more overlapped under aggressive data
augmentations, thus simply aligning the positive samples (augmented views of
the same sample) could make contrastive learning cluster intra-class samples
together. Based on this augmentation overlap perspective, theoretically, we
obtain asymptotically closed bounds for downstream performance under weaker
assumptions, and empirically, we propose an unsupervised model selection metric
ARC that aligns well with downstream accuracy. Our theory suggests an
alternative understanding of contrastive learning: the role of aligning
positive samples is more like a surrogate task than an ultimate goal, and the
overlapped augmented views (i.e., the chaos) create a ladder for contrastive
learning to gradually learn class-separated representations. The code for
computing ARC is available at https://github.com/zhangq327/ARC.


title: PANDORA: Polarization-Aided Neural Decomposition Of Radiance

abstract:
  Reconstructing an object's geometry and appearance from multiple images, also
known as inverse rendering, is a fundamental problem in computer graphics and
vision. Inverse rendering is inherently ill-posed because the captured image is
an intricate function of unknown lighting conditions, material properties and
scene geometry. Recent progress in representing scene properties as
coordinate-based neural networks have facilitated neural inverse rendering
resulting in impressive geometry reconstruction and novel-view synthesis. Our
key insight is that polarization is a useful cue for neural inverse rendering
as polarization strongly depends on surface normals and is distinct for diffuse
and specular reflectance. With the advent of commodity, on-chip, polarization
sensors, capturing polarization has become practical. Thus, we propose PANDORA,
a polarimetric inverse rendering approach based on implicit neural
representations. From multi-view polarization images of an object, PANDORA
jointly extracts the object's 3D geometry, separates the outgoing radiance into
diffuse and specular and estimates the illumination incident on the object. We
show that PANDORA outperforms state-of-the-art radiance decomposition
techniques. PANDORA outputs clean surface reconstructions free from texture
artefacts, models strong specularities accurately and estimates illumination
under practical unstructured scenarios.


title:
Semi-supervised and Deep learning Frameworks for Video Classification
  and Key-frame Identification

abstract:
  Automating video-based data and machine learning pipelines poses several
challenges including metadata generation for efficient storage and retrieval
and isolation of key-frames for scene understanding tasks. In this work, we
present two semi-supervised approaches that automate this process of manual
frame sifting in video streams by automatically classifying scenes for content
and filtering frames for fine-tuning scene understanding tasks. The first
rule-based method starts from a pre-trained object detector and it assigns
scene type, uncertainty and lighting categories to each frame based on
probability distributions of foreground objects. Next, frames with the highest
uncertainty and structural dissimilarity are isolated as key-frames. The second
method relies on the simCLR model for frame encoding followed by
label-spreading from 20% of frame samples to label the remaining frames for
scene and lighting categories. Also, clustering the video frames in the encoded
feature space further isolates key-frames at cluster boundaries. The proposed
methods achieve 64-93% accuracy for automated scene categorization for outdoor
image videos from public domain datasets of JAAD and KITTI. Also, less than 10%
of all input frames can be filtered as key-frames that can then be sent for
annotation and fine tuning of machine vision algorithms. Thus, the proposed
framework can be scaled to additional video data streams for automated training
of perception-driven systems with minimal training images.


title: Hamilton Cycles In Primitive Graphs of Order $2rs$

abstract:
  After long term efforts, it was recently proved in \cite{DKM2} that except
for the Peterson graph, every connected vertex-transitive graph of order $rs$
has a Hamilton cycle, where $r$ and $s$ are primes. A natural topic is to solve
the hamiltonian problem for connected vertex-transitive graphs of $2rs$. This
topic is quite trivial, as the problem is still unsolved even for that of
$r=3$. In this paper, it is shown that except for the Coxeter graph, every
connected vertex-transitive graph of order $2rs$ contains a Hamilton cycle,
provided the automorphism group acts primitively on vertices.


title:
Interpretation of Chest x-rays affected by bullets using deep transfer
  learning

abstract:
  The potential of deep learning, especially in medical imaging, initiated
astonishing results and improved the methodologies after every passing day.
Deep learning in radiology provides the opportunity to classify, detect and
segment different diseases automatically. In the proposed study, we worked on a
non-trivial aspect of medical imaging where we classified and localized the
X-Rays affected by bullets. We tested Images on different classification and
localization models to get considerable accuracy. The replicated data set used
in the study was replicated on different images of chest X-Rays. The proposed
model worked not only on chest radiographs but other body organs X-rays like
leg, abdomen, head, even the training dataset based on chest radiographs.
Custom models have been used for classification and localization purposes after
tuning parameters. Finally, the results of our findings manifested using
different frameworks. This might assist the research enlightening towards this
field. To the best of our knowledge, this is the first study on the detection
and classification of radiographs affected by bullets using deep learning.


title: Pairing core swelling effect in Pb isotopes at $N>126$

abstract:
  We revisit a sudden increase of the isotope shift of the charge radius of Pb
isotope at $N>126$ based on a Skyrme Hartree-Fock-Bogoliubov theory. New
parametrizations of the pairing interaction optimized for selected four Skyrme
interactions greatly improve a description of this phenomenon. The
density-dependent spin-orbit interaction is also investigated and further
increases the charge radius. The pairing correlations significantly change the
properties of the neutron orbits near the Fermi level and play a vital role in
pulling out the well-bound protons in the Pb isotopes at $N>126$. Regarding
$^{208}$Pb as a "core" nucleus, a novel pairing core swelling effect is
proposed: The pairing interaction reduces the radius of "valence" neutron
orbits by the shrinkage of diffused $1g_{9/2}$ orbit and the mixing of sharp
$0i_{11/2}$ orbit. Simultaneously, the core nucleus swells, leading to the
sudden enhancement of the charge radius at $N > 126$. This characteristic
behavior appears in the density profile near the nuclear surface and its
measurement is highly desirable.