title: Recollections of John Wheeler

abstract:
  Recollections by the author about how his interactions with John Wheeler
influenced his career in physics.


title:
Theoretical analysis of magnetic properties and the magnetocaloric
  effect using the Blume-Capel model

abstract:
  This work investigates the magnetic properties and the magnetocaloric effect
in the spin-1 Blume-Capel model. The study was carried out using the mean-field
theory from the Bogoliubov inequality to obtain the expressions of free energy,
magnetization and entropy. The magnetocaloric effect was calculated from the
variation of the entropy obtained by the mean-field theory. Due to the
dependence on the external magnetic field and the anisotropy included in the
model, the results for the magnetocaloric effect provided the system with
first-order and continuous phase transitions. To ensure the results, the
Maxwell relations were used in the intervals where the model presents
continuous variations in magnetization and the Clausius-Clapeyron equation in
the intervals where the model presents discontinuity in the magnetization. The
methods and models for the analysis of a magnetic entropy change and
first-order and continuous magnetic phase transitions, such as mean-field
theory and the Blume-Capel model, are useful tools in understanding the nature
of the magnetocaloric effect and its physical relevance.


title: Resonance pressure of electromagnetic radiation on metal nanoparticle

abstract:
  The influence of the electromagnetic irradiation pressure on a spheroidal
metallic nanoparticle at the frequencies close to the surface plasmon
vibrations has been considered. With the action of the radiation pressure, the
polarizability of metal nanoparticle becomes a tensor quantity. The expressions
for the resonance pressure components for the cases of plane-polarized and
circularly polarized light have been derived. We have demonstrated that the
resonance pressure can substantially depend on the shape of a non-spherical
nanoparticle and its orientation with respect to the direction of light
propagation and the light polarization.


title:
Discovering dynamical features of Hodgkin-Huxley-type model of
  physiological neuron using artificial neural network

abstract:
  We consider Hodgkin-Huxley-type model that is a stiff ODE system with two
fast and one slow variables. For the parameter ranges under consideration the
original version of the model has unstable fixed point and the oscillating
attractor that demonstrates bifurcation from bursting to spiking dynamics. Also
a modified version is considered where the bistability occurs such that an area
in the parameter space appears where the fixed point becomes stable and
coexists with the bursting attractor. For these two systems we create
artificial neural networks that are able to reproduce their dynamics. The
created networks operate as recurrent maps and are trained on trajectory cuts
sampled at random parameter values within a certain range. Although the
networks are trained only on oscillatory trajectory cuts, it also discover the
fixed point of the considered systems. The position and even the eigenvalues
coincide very well with the fixed point of the initial ODEs. For the bistable
model it means that the network being trained only on one brunch of the
solutions recovers another brunch without seeing it during the training. These
results, as we see it, are able to trigger the development of new approaches to
complex dynamics reconstruction and discovering. From the practical point of
view reproducing dynamics with the neural network can be considered as a sort
of alternative method of numerical modeling intended for use with contemporary
parallel hard- and software.


title:
Metaphors in Pre-Trained Language Models: Probing and Generalization
  Across Datasets and Languages

abstract:
  Human languages are full of metaphorical expressions. Metaphors help people
understand the world by connecting new concepts and domains to more familiar
ones. Large pre-trained language models (PLMs) are therefore assumed to encode
metaphorical knowledge useful for NLP systems. In this paper, we investigate
this hypothesis for PLMs, by probing metaphoricity information in their
encodings, and by measuring the cross-lingual and cross-dataset generalization
of this information. We present studies in multiple metaphor detection datasets
and in four languages (i.e., English, Spanish, Russian, and Farsi). Our
extensive experiments suggest that contextual representations in PLMs do encode
metaphorical knowledge, and mostly in their middle layers. The knowledge is
transferable between languages and datasets, especially when the annotation is
consistent across training and testing sets. Our findings give helpful insights
for both cognitive and NLP scientists.


title:
Network of Low-cost Air Quality Sensor for Monitoring Indoor, Outdoor,
  and Personal PM2.5 Exposure in Seattle during the 2020 Wildfire Season

abstract:
  The increased frequency of wildfires in the Western United States has raised
public concerns. Exposure to wildfire smoke has been linked to an increased
risk of cancer and cardiorespiratory morbidity. Evidence-driven interventions
can alleviate the adverse health impact of wildfire smoke. Public health
guidance during wildfires is based on regional air quality data with limited
spatiotemporal resolution. We demonstrate the use of a network of low-cost
particulate matter (PM) sensors to gather indoor, outdoor, and personal PM2.5
exposure data from seven locations in the urban Seattle area, along with a
personal exposure monitor worn by a resident living in one of these locations
during the 2020 Washington wildfire event. The data were used to determine PM
concentration indoor/outdoor (I/O) ratios, PM reduction, and personal exposure
levels. The result shows that locations equipped with high-efficiency
particulate air (HEPA) filters and HVAC filtration systems had significantly
lower I/O ratios (median I/O = 0.43) than those without air filtration (median
I/O = 0.82). The median PM2.5 reduction for the locations with HEPA is 58 %
compared to 20% for the locations without HEPA. The outdoor PM sensors showed a
high correlation to the nearby regional air quality monitoring stations (R2 =
0.93). The personal monitor showed high variance in PM measurements as the user
moved through different microenvironments and could not be fully characterized
by the network of indoor or outdoor monitors. The findings imply evidence-based
interventions can be developed for reducing pollution exposure based on the
combination of indoor, outdoor sensors. Personal exposure monitoring in
individuals' breathing zones provided the highest fidelity data capturing
temporal spikes in PM exposure.


title:
Efficient Global Robustness Certification of Neural Networks via
  Interleaving Twin-Network Encoding

abstract:
  The robustness of deep neural networks has received significant interest
recently, especially when being deployed in safety-critical systems, as it is
important to analyze how sensitive the model output is under input
perturbations. While most previous works focused on the local robustness
property around an input sample, the studies of the global robustness property,
which bounds the maximum output change under perturbations over the entire
input space, are still lacking. In this work, we formulate the global
robustness certification for neural networks with ReLU activation functions as
a mixed-integer linear programming (MILP) problem, and present an efficient
approach to address it. Our approach includes a novel interleaving twin-network
encoding scheme, where two copies of the neural network are encoded
side-by-side with extra interleaving dependencies added between them, and an
over-approximation algorithm leveraging relaxation and refinement techniques to
reduce complexity. Experiments demonstrate the timing efficiency of our work
when compared with previous global robustness certification methods and the
tightness of our over-approximation. A case study of closed-loop control safety
verification is conducted, and demonstrates the importance and practicality of
our approach for certifying the global robustness of neural networks in
safety-critical systems.


title: Heat kernel asymptotics for real powers of Laplacians

abstract:
  We describe the small-time heat kernel asymptotics of real powers $\Delta^r$,
$r \in (0,1)$ of a non-negative self-adjoint generalized Laplacian $\Delta$
acting on the sections of a hermitian vector bundle $\mathcal E$ over a compact
manifold $M$. First we treat separately the asymptotic on the diagonal of $M
\times M$ and in a compact away from it. Logarithmic terms appear only if $n$
is odd and $r$ is rational with even denominator. We prove the non-triviality
of the coefficients appearing in the diagonal asymptotics, and also the
non-locallity of some of the coefficients. In the special case $r=1/2$, we give
a simultaneous formula by proving that the heat kernel of $\Delta^{1/2}$ is a
polyhomogeneous conormal section in $\mathcal E \boxtimes \mathcal E^* $ on the
blow-up $M_{heat}$ of the diagonal at time $0$ inside $[0,\infty)\times M
\times M$.


title:
The Mean Field Fokker-Planck Equation with Nonlinear No-flux Boundary
  Conditions

abstract:
  We consider the mean field Fokker-Planck equation subject to nonlinear
no-flux boundary conditions, which necessarily arise when subjecting a system
of Brownian particles interacting via a pair potential in a bounded domain.
With the additional presence of an external potential $V_1$, we show, by
analysing the linearised Fokker-Planck operator, that the spectral properties
of the equilibrium densities can differ considerably when compared with
previous studies, e.g., with periodic boundary conditions. Amongst other mean
field models of complex many-body particle systems, we present numerical
experiments encompassing in a wide range of physical applications, including:
generalised exponential models (Gaussian, Morse); a Kuramoto model, for noisy
coupled oscillators; and an Onsager model for liquid crystals. We showcase our
results by using the numerical methods developed in the pseudospectral
collocation scheme 2DChebClass.


title:
Demonstrating CAT: Synthesizing Data-Aware Conversational Agents for
  Transactional Databases

abstract:
  Databases for OLTP are often the backbone for applications such as hotel room
or cinema ticket booking applications. However, developing a conversational
agent (i.e., a chatbot-like interface) to allow end-users to interact with an
application using natural language requires both immense amounts of training
data and NLP expertise. This motivates CAT, which can be used to easily create
conversational agents for transactional databases. The main idea is that, for a
given OLTP database, CAT uses weak supervision to synthesize the required
training data to train a state-of-the-art conversational agent, allowing users
to interact with the OLTP database. Furthermore, CAT provides an out-of-the-box
integration of the resulting agent with the database. As a major difference to
existing conversational agents, agents synthesized by CAT are data-aware. This
means that the agent decides which information should be requested from the
user based on the current data distributions in the database, which typically
results in markedly more efficient dialogues compared with non-data-aware
agents. We publish the code for CAT as open source.


title: Reverse Engineering of Imperceptible Adversarial Image Perturbations

abstract:
  It has been well recognized that neural network based image classifiers are
easily fooled by images with tiny perturbations crafted by an adversary. There
has been a vast volume of research to generate and defend such adversarial
attacks. However, the following problem is left unexplored: How to
reverse-engineer adversarial perturbations from an adversarial image? This
leads to a new adversarial learning paradigm--Reverse Engineering of Deceptions
(RED). If successful, RED allows us to estimate adversarial perturbations and
recover the original images. However, carefully crafted, tiny adversarial
perturbations are difficult to recover by optimizing a unilateral RED
objective. For example, the pure image denoising method may overfit to
minimizing the reconstruction error but hardly preserve the classification
properties of the true adversarial perturbations. To tackle this challenge, we
formalize the RED problem and identify a set of principles crucial to the RED
approach design. Particularly, we find that prediction alignment and proper
data augmentation (in terms of spatial transformations) are two criteria to
achieve a generalizable RED approach. By integrating these RED principles with
image denoising, we propose a new Class-Discriminative Denoising based RED
framework, termed CDD-RED. Extensive experiments demonstrate the effectiveness
of CDD-RED under different evaluation metrics (ranging from the pixel-level,
prediction-level to the attribution-level alignment) and a variety of attack
generation methods (e.g., FGSM, PGD, CW, AutoAttack, and adaptive attacks).


title: Pettis property for Polish inverse semigroups

abstract:
  We study a property about Polish inverse semigroups similar to the classical
theorem of Pettis about Polish groups. In contrast to what happens with Polish
groups, not every Polish inverse semigroup have the Pettis property. We present
several examples of Polish inverse subsemigroup of the symmetric inverse
semigroup I(N) of all partial bijections between subsets of N. We also study
whether our examples satisfy automatic continuity.


title: Algorithmic correspondence and analytic rules

abstract:
  We introduce the algorithm MASSA which takes classical modal formulas in
input, and, when successful, effectively generates: (a) (analytic) geometric
rules of the labelled calculus G3K, and (b) cut-free derivations (of a certain
`canonical' shape) of each given input formula in the geometric labelled
calculus obtained by adding the rule in output to G3K. We show that MASSA
successfully terminates whenever its input formula is a (definite) analytic
inductive formula, in which case, the geometric axiom corresponding to the
output rule is, modulo logical equivalence, the first-order correspondent of
the input formula.


title:
Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image
  Matching

abstract:
  We address the problem of ground-to-satellite image geo-localization, that
is, estimating the camera latitude, longitude and orientation (azimuth angle)
by matching a query image captured at the ground level against a large-scale
database with geotagged satellite images. Our prior arts treat the above task
as pure image retrieval by selecting the most similar satellite reference image
matching the ground-level query image. However, such an approach often produces
coarse location estimates because the geotag of the retrieved satellite image
only corresponds to the image center while the ground camera can be located at
any point within the image. To further consolidate our prior research findings,
we present a novel geometry-aware geo-localization method. Our new method is
able to achieve the fine-grained location of a query image, up to pixel size
precision of the satellite image, once its coarse location and orientation have
been determined. Moreover, we propose a new geometry-aware image retrieval
pipeline to improve the coarse localization accuracy. Apart from a polar
transform in our conference work, this new pipeline also maps satellite image
pixels to the ground-level plane in the ground-view via a geometry-constrained
projective transform to emphasize informative regions, such as road structures,
for cross-view geo-localization. Extensive quantitative and qualitative
experiments demonstrate the effectiveness of our newly proposed framework. We
also significantly improve the performance of coarse localization results
compared to the state-of-the-art in terms of location recalls.


title:
Odd Grassmannian bimodules and derived equivalences for spin symmetric
  groups

abstract:
  We prove odd analogs of results of Chuang and Rouquier on
sl(2)-categorification. Combined also with recent work of the second author
with Livesey, this allows us to complete the proof of Brou\'e's Abelian Defect
Conjecture for the double covers of symmetric groups. The article also develops
the theory of odd symmetric functions initiated a decade ago by Ellis, Khovanov
and Lauda. A key role in our approach is played by a 2-supercategory consisting
of odd Grassmannian bimodules over superalgebras which are odd analogs of
equivariant cohomology algebras of Grassmannians. This is the odd analog of the
category of Grassmannian bimodules which was at the heart of Lauda's
independent approach to categorification of sl(2).


title: Nematic colloidal micro-robots as physically intelligent systems

abstract:
  Physically intelligent micro-robotic systems exploit information embedded in
micro-robots, their colloidal cargo, and their milieu to interact, assemble and
form functional structures. Nonlinear anisotropic fluids like nematic liquid
crystals (NLCs) provide untapped opportunities to embed interactions via their
topological defects, complex elastic responses, and their ability to
dramatically restructure in dynamic settings. Here we design and fabricate a
4-armed ferromagnetic micro-robot to embed and dynamically reconfigure
information in the nematic director field, generating a suite of physical
interactions for cargo manipulation. The micro-robot shape and surface
chemistry are designed to generate a nemato-elastic energy landscape in the
domain that defines multiple modes of emergent, bottom-up interactions with
passive colloids. Micro-robot rotation expands the ability to sculpt
interactions; the energy landscape around a rotating micro-robot is dynamically
reconfigured by complex far-from-equilibrium dynamics of the micro-robot's
companion topological defect. These defect dynamics allow transient information
to be programmed into the domain and exploited for top-down cargo manipulation.
We demonstrate robust micro-robotic manipulation strategies that exploit these
diverse modes of nemato-elastic interaction to achieve cargo docking,
transport, release, and assembly of complex reconfigurable structures at
multi-stable sites. Such structures are of great interest to future
developments of LC-based advanced optical device and micro-manufacturing in
anisotropic environments.


title:
Room-temperature strong coupling at the nanoscale achieved by inverse
  design

abstract:
  Room-temperature strong coupling between plasmonic nanocavities and monolayer
semiconductors is a prominent path towards efficient, integrated light-matter
interactions. However, designing such systems is challenging due to the
nontrivial dependence of the strong coupling on various properties of the
cavity and emitter, as well as the subwavelength scale of the interaction. In
this work, we develop a methodology for obtaining hybrid nanostructures
consisting of plasmonic metasurfaces coupled to atomically thin WS2 layers,
exhibiting extreme values of Rabi splitting, by inverse design of the
near-field plasmonic response. Contrary to common measures such as the quality
factor or the mode volume, our method relies on an overlap-integral-based
metric. We experimentally measure large values of Rabi splitting for our
nanoantenna designs, while providing theoretically optimal configurations for
several additional types of nanostructures. Our results open a path to
maximizing light-matter interactions in integrated platforms, for classical and
quantum-optical applications.


title:
Collaborative Intelligent Reflecting Surface Networks with Multi-Agent
  Reinforcement Learning

abstract:
  Intelligent reflecting surface (IRS) is envisioned to be widely applied in
future wireless networks. In this paper, we investigate a multi-user
communication system assisted by cooperative IRS devices with the capability of
energy harvesting. Aiming to maximize the long-term average achievable system
rate, an optimization problem is formulated by jointly designing the transmit
beamforming at the base station (BS) and discrete phase shift beamforming at
the IRSs, with the constraints on transmit power, user data rate requirement
and IRS energy buffer size. Considering time-varying channels and stochastic
arrivals of energy harvested by the IRSs, we first formulate the problem as a
Markov decision process (MDP) and then develop a novel multi-agent Q-mix (MAQ)
framework with two layers to decouple the optimization parameters. The higher
layer is for optimizing phase shift resolutions, and the lower one is for phase
shift beamforming and power allocation. Since the phase shift optimization is
an integer programming problem with a large-scale action space, we improve MAQ
by incorporating the Wolpertinger method, namely, MAQ-WP algorithm to achieve a
sub-optimality with reduced dimensions of action space. In addition, as MAQ-WP
is still of high complexity to achieve good performance, we propose a policy
gradient-based MAQ algorithm, namely, MAQ-PG, by mapping the discrete phase
shift actions into a continuous space at the cost of a slight performance loss.
Simulation results demonstrate that the proposed MAQ-WP and MAQ-PG algorithms
can converge faster and achieve data rate improvements of 10.7% and 8.8% over
the conventional multi-agent DDPG, respectively.


title:
Derived superequivalences for spin symmetric groups and odd
  sl(2)-categorifications

abstract:
  We show that actions of the odd categorification of sl(2) induce derived
superequivalences analogous to those introduced by Chuang and Rouquier. Using
Kang, Kashiwara, and Oh's action of the odd 2-category on blocks of the
cyclotomic affine Hecke-Clifford algebra, our equivalences imply that blocks
related by a certain affine Weyl group action are derived equivalent. By recent
results of Kleshchev and Livesey, we show this implies Broue's abelian defect
conjecture for the modular representations of the spin symmetric group.


title: NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows

abstract:
  Deep Learning (DL) algorithms are becoming increasingly popular for the
reconstruction of high-resolution turbulent flows (aka super-resolution).
However, current DL approaches perform spatially uniform super-resolution - a
key performance limiter for scalability of DL-based surrogates for
Computational Fluid Dynamics (CFD).
  To address the above challenge, we introduce NUNet, a deep learning-based
adaptive mesh refinement (AMR) framework for non-uniform super-resolution of
turbulent flows. NUNet divides the input low-resolution flow field into
patches, scores each patch, and predicts their target resolution. As a result,
it outputs a spatially non-uniform flow field, adaptively refining regions of
the fluid domain to achieve the target accuracy. We train NUNet with
Reynolds-Averaged Navier-Stokes (RANS) solutions from three different canonical
flows, namely turbulent channel flow, flat plate, and flow around ellipses.
NUNet shows remarkable discerning properties, refining areas with complex flow
features, such as near-wall domains and the wake region in flow around solid
bodies, while leaving areas with smooth variations (such as the freestream) in
the low-precision range. Hence, NUNet demonstrates an excellent qualitative and
quantitative alignment with the traditional OpenFOAM AMR solver. Moreover, it
reaches the same convergence guarantees as the AMR solver while accelerating it
by 3.2-5.5x, including unseen-during-training geometries and boundary
conditions, demonstrating its generalization capacities. Due to NUNet's ability
to super-resolve only regions of interest, it predicts the same target
1024x1024 spatial resolution 7-28.5x faster than state-of-the-art DL methods
and reduces the memory usage by 4.4-7.65x, showcasing improved scalability.


title: How Do We Fail? Stress Testing Perception in Autonomous Vehicles

abstract:
  Autonomous vehicles (AVs) rely on environment perception and behavior
prediction to reason about agents in their surroundings. These perception
systems must be robust to adverse weather such as rain, fog, and snow. However,
validation of these systems is challenging due to their complexity and
dependence on observation histories. This paper presents a method for
characterizing failures of LiDAR-based perception systems for AVs in adverse
weather conditions. We develop a methodology based in reinforcement learning to
find likely failures in object tracking and trajectory prediction due to
sequences of disturbances. We apply disturbances using a physics-based data
augmentation technique for simulating LiDAR point clouds in adverse weather
conditions. Experiments performed across a wide range of driving scenarios from
a real-world driving dataset show that our proposed approach finds high
likelihood failures with smaller input disturbances compared to baselines while
remaining computationally tractable. Identified failures can inform future
development of robust perception systems for AVs.


title:
SpeechSplit 2.0: Unsupervised speech disentanglement for voice
  conversion Without tuning autoencoder Bottlenecks

abstract:
  SpeechSplit can perform aspect-specific voice conversion by disentangling
speech into content, rhythm, pitch, and timbre using multiple autoencoders in
an unsupervised manner. However, SpeechSplit requires careful tuning of the
autoencoder bottlenecks, which can be time-consuming and less robust. This
paper proposes SpeechSplit 2.0, which constrains the information flow of the
speech component to be disentangled on the autoencoder input using efficient
signal processing methods instead of bottleneck tuning. Evaluation results show
that SpeechSplit 2.0 achieves comparable performance to SpeechSplit in speech
disentanglement and superior robustness to the bottleneck size variations. Our
code is available at https://github.com/biggytruck/SpeechSplit2.


title:
Direct Numerical Simulation of High Prandtl Number Fluid Flow in the
  Downcomer of an Advanced Reactor

abstract:
  The passive safety is a crucial feature of advanced nuclear reactor (Gen IV)
design. During loss of power scenarios, the downcomer plays a crucial role. The
fluid-flow behavior in the downcomer can involve forced to mixed to natural
convection and characterizing the heat transfer for these changing regimes is a
daunting challenge. High-resolution heat transfer numerical database can
potentially support the development of precise and affordable reduced
resolution heat transfer models. These models can be designed based on a
multiscale hierarchy developed as part of the recently DOE-funded center of
excellence for thermal-fluids applications in nuclear energy. In this paper,
the downcomer is simplified to heated parallel plates and High Prandtl number
fluid (FLiBe) is considered for all simulations. The calculations are performed
for a wide range of Richardson number from 0 to 400 at 2 different FLiBe
Prandtl numbers (12 and 24) which result in 40 simulated Cases in total. Time
averaged and time series statistics as well as Nusselt number correlations are
investigated to illuminate the mixed convection behaviors. The calculated
database will be instrumental in understanding the flow behaviors in the
downcomer. Ultimately, we aimed to evaluate existing heat transfer correlations
and some modifications will be proposed for Cases where no satisfactory choice
is available.


title:
Predicting the impact of formation protocols on battery lifetime
  immediately after manufacturing

abstract:
  Increasing the speed of battery formation can significantly lower lithium-ion
battery manufacturing costs. However, adopting faster formation protocols in
practical manufacturing settings is challenging due to a lack of inexpensive,
rapid diagnostic signals that can inform possible impacts to long-term battery
lifetime. This work identifies the cell resistance measured at low states of
charge as an early-life diagnostic feature for screening new formation
protocols. We show that this signal correlates to cycle life and improves the
accuracy of data-driven battery lifetime prediction models. The signal is
obtainable at the end of the manufacturing line, takes seconds to acquire, and
does not require specialized test equipment. We explore a physical connection
between this resistance signal and the quantity of lithium consumed during
formation, suggesting that the signal may be broadly applicable for evaluating
any manufacturing process change that could impact the total lithium consumed
during formation.


title:
A Novel Neuromorphic Processors Realization of Spiking Deep
  Reinforcement Learning for Portfolio Management

abstract:
  The process of continuously reallocating funds into financial assets, aiming
to increase the expected return of investment and minimizing the risk, is known
as portfolio management. Processing speed and energy consumption of portfolio
management have become crucial as the complexity of their real-world
applications increasingly involves high-dimensional observation and action
spaces and environment uncertainty, which their limited onboard resources
cannot offset. Emerging neuromorphic chips inspired by the human brain increase
processing speed by up to 1000 times and reduce power consumption by several
orders of magnitude. This paper proposes a spiking deep reinforcement learning
(SDRL) algorithm that can predict financial markets based on unpredictable
environments and achieve the defined portfolio management goal of profitability
and risk reduction. This algorithm is optimized forIntel's Loihi neuromorphic
processor and provides 186x and 516x energy consumption reduction is observed
compared to the competitors, respectively. In addition, a 1.3x and 2.0x
speed-up over the high-end processors and GPUs, respectively. The evaluations
are performed on cryptocurrency market between 2016 and 2021 the benchmark.


title:
Metal-Insulator Transition and Anomalous Lattice Parameters Changes in
  Ru-doped VO2

abstract:
  VO2, of interest for decades due to both its phenomenology and its potential
applications, has a monoclinic distortion of the rutile crystal structure at
ambient temperature that is coupled to its metal-insulator transition (MIT). In
contrast, RuO2 has three electrons more per formula unit, is a metallic
conductor, and has an undistorted rutile structure. Here we report a systematic
study of Ru-doped VO2; generally characterizing its crystal structure, magnetic
and electronic properties, and heat capacity. The composition-dependent Wilson
ratio is determined. We find that an unusually high Ru doping value (80%,
x=0.8) is required to suppress the MIT in VO2. No superconductivity was
observed down to 0.1 K in the metallic materials. We propose a possible
understanding for how the insulating state can exist in V1-xRuxO2 at high Ru
contents.


title: Properties of EUV Imaging Spectrometer (EIS) Slot Observations

abstract:
  The Extreme ultraviolet Imaging Spectrometer (EIS) on board the Hinode
spacecraft has been operating since 2006, returning high resolution data in the
170-212 and 246-292 A wavelength regions. EIS has four slit options, with the
narrow 1" and 2" slits used for spectroscopy and the wide 40" and 266" slits
used for monochromatic imaging. In this article several properties of the 40"
slit (or slot) are measured using the Fe XII 195.12 A line, which is formed at
1.5 MK. The projected width of the slot on the detector shows a small variation
along the slit with an average value of 40.949". The slot image is tilted on
the detector and a quadratic formula is provided to describe the tilt. The tilt
corresponds to four pixels on the detector and the slot centroid is offset
mostly to the right (longer wavelengths) of the 1" slit by up to four pixels.
Measurement of the intensity decrease at the edge of the slot leads to an
estimate of the spatial resolution of the images in the x-direction. The
resolution varies quadratically along the slot, with a minimum value of 2.9"
close to the detector center. Intensities measured from the slot images are
found to be on average 14% higher than those measured from the 1" slit at the
same spatial location. Background subtraction is necessary to derive accurate
intensities in quiet Sun and coronal hole regions. Prescriptions for deriving
accurate slot intensities for different types of slot datasets are presented.


title: $\bar{Q}'$-curvature flow on Pseudo-Einstein CR manifolds

abstract:
  In this paper we consider the problem of prescribing the $\bar{Q}'$-curvature
on three dimensional Pseudo-Einstein CR manifolds. We study the gradient flow
generated by the related functional and we will prove its convergence to a
limit function under suitable assumptions.


title: Compactness of Dirac-Einstein spin manifolds and horizontal deformations

abstract:
  In this paper we consider the Hilbert-Einstein-Dirac functional, whose
critical points are pairs, metrics-spinors, that satisfy a system coupling the
Riemannian and the spinorial part. Under some assumptions, on the sign of the
scalar curvature and the diameter, we prove a compactness result for this class
of pairs, in dimension three and four. This can be seen as the equivalent of
the study of compactness of sequences of Einstein manifolds as in
\cite{And0,N}. Indeed, we study the compactness of sequences of critical points
of the Hilbert-Einstein-Dirac functional which is an extension of the
Hilbert-Einstein functional having Einstein manifolds as critical points.
Moreover we will study the second variation of the energy, characterizing the
horizontal deformations for which the second variation vanishes. Finally we
will exhibit some explicit examples.


title:
Asymptotic formula for Tate-Shafarevich groups of $p$-supersingular
  elliptic curves over anticyclotomic extensions

abstract:
  Let $p\ge 5$ be a prime number and $E/\mathbf{Q}$ an elliptic curve with good
supersingular reduction at $p$. Under the generalized Heegner hypothesis, we
investigate the $p$-primary subgroups of the Tate--Shafarevich groups of $E$
over number fields contained inside the anticyclotomic $\mathbf{Z}_p$-extension
of an imaginary quadratic field where $p$ splits.


title:
A Robust Optimization Method for Label Noisy Datasets Based on Adaptive
  Threshold: Adaptive-k

abstract:
  SGD does not produce robust results on datasets with label noise. Because the
gradients calculated according to the losses of the noisy samples cause the
optimization process to go in the wrong direction. In this paper, as an
alternative to SGD, we recommend using samples with loss less than a threshold
value determined during the optimization process, instead of using all samples
in the mini-batch. Our proposed method, Adaptive-k, aims to exclude label noise
samples from the optimization process and make the process robust. On noisy
datasets, we found that using a threshold-based approach, such as Adaptive-k,
produces better results than using all samples or a fixed number of low-loss
samples in the mini-batch. Based on our theoretical analysis and experimental
results, we show that the Adaptive-k method is closest to the performance of
the oracle, in which noisy samples are entirely removed from the dataset.
Adaptive-k is a simple but effective method. It does not require prior
knowledge of the noise ratio of the dataset, does not require additional model
training, and does not increase training time significantly. The code for
Adaptive-k is available at https://github.com/enesdedeoglu-TR/Adaptive-k


title: Competition on $\mathbb{Z}^d$ driven by branching random walk

abstract:
  A competition process on $\mathbb{Z}^d$ is considered, where two species
compete to color the sites. The entities are driven by branching random walks.
Specifically red (blue) particles reproduce in discrete time and place
offspring according to a given reproduction law, which may be different for the
two types. When a red (blue) particle is placed at a site that has not been
occupied by any particle before, the site is colored red (blue) and keeps this
color forever. The types interact in that, when a particle is placed at a site
of opposite color, the particle adopts the color of the site with probability
$p\in[0,1]$. Can a given type color infinitely many sites? Can both types color
infinitely many sites simultaneously? Partial answers are given to these
questions and many open problems are formulated.


title:
Distributed Detection Fusion in Clustered Sensor Networks over Multiple
  Access Fading Channels

abstract:
  In this paper, we tackle decision fusion for distributed detection in a
randomly-deployed clustered wireless sensor networks (WSNs) operating over a
non-ideal multiple access channels (MACs), i.e. considering Rayleigh fading,
pathloss and additive noise. To mitigate fading, we propose the distributed
equal gain transmit combining (dEGTC) and distributed maximum ratio transit
combining (dMRTC). The first and second order statistics of the received
signals were analytically computed via stochastic geometry tools. Then the
distribution of the received signal over the MAC are approximated by Gaussian
and log-normal distributions via moment matching. This enabled the derivation
of moment matching optimal fusion rules (MOR)for both distributions. Moreover,
suboptimal simpler fusion rules were also proposed, in which all the CHs data
are equally weighed, which is termed moment matching equal gain fusion rule
(MER). It is shown by simulations that increasing the number of clusters
improve the performance. Moreover, MOR-Gaussian based algorithms are better
under free-space propagation whereas their lognormal counterparts are more
suited in the ground-reflection case. Also, the latter algorithms show better
results in low SNR and SN numbers conditions. We have proved that the received
power at the CH in MAC is proportional O(2R2) and to O(2ln2R) in the free-space
propagation and the ground-reflection cases respectively, whereis SN deployment
intensity and R is the cluster radius. This implies that having more clusters
decreases the required transmission power for a given SNR at the receiver.


title: A construction of exotic metallic states

abstract:
  We discuss examples of two dimensional metallic states with charge
fractionalization, and we will demonstrate that the mechanism of charge
fractionalization leads to exotic metallic behaviors at low and intermediate
temperature. The simplest example of such state is constructed by fermionic
partons at finite density coupled to a $Z_N$ gauge field, whose properties can
be studied through rudimentary methods. This simple state has the following
exotic features: (1) at low temperature this state is a "bad metal" whose
resistivity can exceed the Mott-Ioffe-Regel limit; (2) while increasing
temperature $T$ the resistivity $\rho(T)$ is a nonmonotonic function, and it
crosses over from a bad metal at low $T$ to a good metal at relatively high
$T$; (3) the optical conductivity $\sigma(\omega)$ has a small Drude weight at
low $T$, and a larger Drude weight at intermediate $T$; (4) at low temperature
the metallic state has a large Lorenz number, which strongly violates the
Wiedemann-Franz law. A more complex example with fermionic partons at finite
density coupled to a SU(N) gauge field will also be constructed.


title:
AutoTS: Automatic Time Series Forecasting Model Design Based on
  Two-Stage Pruning

abstract:
  Automatic Time Series Forecasting (TSF) model design which aims to help users
to efficiently design suitable forecasting model for the given time series data
scenarios, is a novel research topic to be urgently solved. In this paper, we
propose AutoTS algorithm trying to utilize the existing design skills and
design efficient search methods to effectively solve this problem. In AutoTS,
we extract effective design experience from the existing TSF works. We allow
the effective combination of design experience from different sources, so as to
create an effective search space containing a variety of TSF models to support
different TSF tasks. Considering the huge search space, in AutoTS, we propose a
two-stage pruning strategy to reduce the search difficulty and improve the
search efficiency. In addition, in AutoTS, we introduce the knowledge graph to
reveal associations between module options. We make full use of these
relational information to learn higher-level features of each module option, so
as to further improve the search quality. Extensive experimental results show
that AutoTS is well-suited for the TSF area. It is more efficient than the
existing neural architecture search algorithms, and can quickly design powerful
TSF model better than the manually designed ones.


title:
Tailoring the Thickness-Dependent Optical Properties of Conducting
  Nitrides and Oxides for Epsilon-Near-Zero-Enhanced Photonic Applications

abstract:
  The unique properties of the emerging photonic materials - conducting
nitrides and oxides - especially their tailorability, large damage thresholds,
and the so-called epsilon-near-zero (ENZ) behavior, have enabled novel photonic
phenomena spanning optical circuitry, tunable metasurfaces, and nonlinear
optical devices. This work explores direct control of the optical properties of
polycrystalline titanium nitride (TiN) and aluminum-doped zinc oxide (AZO) by
tailoring the film thickness, and their potential for ENZ-enhanced photonic
applications. We demonstrate that TiN-AZO bilayers act as Ferrell-Berreman
metasurfaces with thickness-tailorable epsilon-near-zero resonances in the AZO
films operating in the telecom wavelengths spanning from 1470 to 1750 nm. The
bilayer stacks also act as strong light absorbers in the ultraviolet regime
employing the radiative ENZ modes and the Fabry-Perot modes in the constituent
TiN films. The studied Berreman metasurfaces exhibit optically-induced
reflectance modulation of 15% with picosecond response-time. Together with the
optical response tailorability of conducting oxides and nitrides, utilizing the
field-enhancement near the tunable ENZ regime could enable a wide range of
nonlinear optical phenomena, including all-optical switching, time refraction,
and high-harmonic generation.


title:
Towards Privacy-Preserving Speech Representation for Client-Side Data
  Sharing

abstract:
  Privacy and security are major concerns when sharing and collecting speech
data for cloud services such as automatic speech recognition (ASR) and speech
emotion recognition (SER). Existing solutions for client-side privacy mainly
focus on voice conversion or voice modification to convert a raw utterance into
another one with similar content but different, or no, identity-related
information. However, an alternative approach to share speech data under the
form of privacy-preserving representations has been largely under-explored. To
fill this gap, we propose a speech anonymization framework that provides formal
privacy guarantees via noise perturbation to a selected subset of the
high-utility representations extracted using a pre-trained speech encoder. The
subset is chosen with a Transformer-based privacy-risk saliency estimator. We
validate our framework on four tasks, namely, Automatic Speaker Verification
(ASV), ASR, SER and Intent Classification (IC) for privacy and utility
assessment. Experimental results show that our approach is able to achieve a
competitive, or even better, utility compared to the baselines that use voice
conversion and voice modification, providing the same level of privacy.
Moreover, the easily-controlled amount of perturbation allows our framework to
have a flexible range of privacy-utility trade-offs without re-training any
components.


title:
Triplet vector boson model explanation to the $B$ meson anomalies: An
  updated view and perspectives

abstract:
  The triplet vector boson (TVB) is a simplified new physics model involving
massive vector bosons transforming as a weak triplet vector, which it has been
proposed as a combined explanation to the anomalous $b \to s\mu^+\mu^-$ and $b
\to c \tau\bar{\nu}_\tau$ data (the so-called $B$ meson anomalies). In this
work, we carry out an updated view of the TVB model, including the Belle II
perspectives. We perform a global fit to explore the allowed parameter space by
the most current $b \to s\mu^+\mu^-$ and $b \to c \tau\bar{\nu}_\tau$ data, by
considering all relevant low-energy flavor observables. Our results are
confronted with the most recent LHC constraints. We also incorporate in our
study the first measurement on the ratio $R(\Lambda_c) = {\rm BR}(\Lambda_b \to
\Lambda_c\tau\bar{\nu}_\tau)/{\rm BR}(\Lambda_b \to \Lambda_c\mu\bar{\nu}_\mu)$
very recently obtained by LHCb. In particular, we show that the TVB model can
provide an explanation to the $B$ meson anomalies; however, this framework is
in strong tension with LHC bounds. In respect to future flavor measurements at
Belle II, our results suggest that a small new physics window would be allow to
solely explain the $b \to c \tau \bar{\nu}_\tau$ data in agreement with LHC
constraints. Furthermore, the implications of our phenomenological analysis of
the TVB model to some known flavor parametrizations are also discussed.


title:
Maximal origami flip graphs of flat-foldable vertices: properties and
  algorithms

abstract:
  Flat origami studies straight line, planar graphs $C=(V,E)$ drawn on a region
$R\subset\mathbb{R}^2$ that can act as crease patterns to map, or fold, $R$
into $\mathbb{R}^2$ in a way that is continuous and a piecewise isometry
exactly on the faces of $C$. Associated with such crease pattern graphs are
valid mountain-valley (MV) assignments $\mu:E\to\{-1,1\}$, indicating which
creases can be mountains (convex) or valleys (concave) to allow $R$ to
physically fold flat without self-intersecting. In this paper, we initiate the
first study of how valid MV assignments of single-vertex crease patterns are
related to one another via face-flips, a concept that emerged from applications
of origami in engineering and physics, where flipping a face $F$ means
switching the MV parity of all creases of $C$ that border $F$. Specifically, we
study the origami flip graph ${\rm{OFG}}(C)$, whose vertices are all valid MV
assignments of $C$ and edges connect assignments that differ by only one face
flip. We prove that, for the single-vertex crease pattern $A_{2n}$ whose $2n$
sector angles around the vertex are all equal, ${\rm{OFG}}(A_{2n})$ contains as
subgraphs all other origami flip graphs of degree-$2n$ flat origami vertex
crease patterns. We also prove that ${\rm{OFG}}(A_{2n})$ is connected and has
diameter $n$ by providing two $O(n^2)$ algorithms to traverse between vertices
in the graph, and we enumerate the vertices, edges, and degree sequence of
${\rm{OFG}}(A_{2n})$. We conclude with open questions on the surprising
complexity found in origami flip graphs of this type.


title:
Almost-periodic ground state of the non-self-adjoint Jacobi operator and
  its applications

abstract:
  We study the ground states of the one-dimensional non-self-adjoint Jacobi
operators in the almost periodic media by using the method of dynamical
systems. We show the existence of the ground state. Particularly, in the
quasi-periodic media, we show that the lower regularity of coefficients can
guarantee the existence of ground states. Besides that, we give two
applications: the first application is to show the existence and uniqueness of
the positive steady state of the discrete Fisher-KPP type equation; the second
application is to investigate the asymptotic behavior of the discrete
stationary parabolic equation with large lower order terms.


title:
On the Hilbert Function of a Finite Scheme Contained in a Quadric
  Surface

abstract:
  Consider a finite scheme of length l contained in a smooth quadric surface
over the complex numbers. We determine the number of linearly independent
curves passing through the scheme, of degree at least l - 1.


title:
Ab initio calculation of the $\beta$-decay from $^{11}$Be to a
  p${+}^{10}$Be resonance

abstract:
  The exotic $\beta$-delayed proton emission is calculated in $^{11}$Be from
first principles using chiral two- and three-nucleon forces. To investigate the
unexpectedly-large branching ratio measured in [PRL 123, 082501 (2019)] we
calculate the proposed $(1/2^+,1/2)$ proton resonance in $^{11}$B using the
no-core shell model with continuum. This calculation helps to address whether
this enhancement is caused by unknown dark decay modes or an unobserved proton
resonance. We report a branching ratio of $b_p = (1.3\pm0.5)\times10^{-6}$,
suggesting that its unexpectedly-large value is caused by an unobserved proton
resonance in $^{11}$B.


title:
Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic
  Choices

abstract:
  The area under the ROC curve (AUROC) has been vigorously applied for
imbalanced classification and moreover combined with deep learning techniques.
However, there is no existing work that provides sound information for peers to
choose appropriate deep AUROC maximization techniques. In this work, we fill
this gap from three aspects. (i) We benchmark a variety of loss functions with
different algorithmic choices for deep AUROC optimization problem. We study the
loss functions in two categories: pairwise loss and composite loss, which
includes a total of 10 loss functions. Interestingly, we find composite loss,
as an innovative loss function class, shows more competitive performance than
pairwise loss from both training convergence and testing generalization
perspectives. Nevertheless, data with more corrupted labels favors a pairwise
symmetric loss. (ii) Moreover, we benchmark and highlight the essential
algorithmic choices such as positive sampling rate, regularization,
normalization/activation, and optimizers. Key findings include: higher positive
sampling rate is likely to be beneficial for deep AUROC maximization; different
datasets favors different weights of regularizations; appropriate normalization
techniques, such as sigmoid and $\ell_2$ score normalization, could improve
model performance. (iii) For optimization aspect, we benchmark SGD-type,
Momentum-type, and Adam-type optimizers for both pairwise and composite loss.
Our findings show that although Adam-type method is more competitive from
training perspective, but it does not outperform others from testing
perspective.


title:
Energy Efficient VM Placement in a Heterogeneous Fog Computing
  Architecture

abstract:
  Recent years have witnessed a remarkable development in communication and
computing systems, mainly driven by the increasing demands of data and
processing intensive applications such as virtual reality, M2M, connected
vehicles, IoT services, to name a few. Massive amounts of data will be
collected by various mobile and fixed terminals that will need to be processed
in order to extract knowledge from the data. Traditionally, a centralized
approach is taken for processing the collected data using large data centers
connected to a core network. However, due to the scale of the
Internet-connected things, transporting raw data all the way to the core
network is costly in terms of the power consumption, delay, and privacy. This
has compelled researchers to propose different decentralized computing
paradigms such as fog computing to process collected data at the network edge
close to the terminals and users. In this paper, we study, in a Passive Optical
Network (PON)-based collaborative-fog computing system, the impact of the
heterogeneity of the fog units capacity and energy-efficiency on the overall
energy-efficiency of the fog system. We optimized the virtual machine (VM)
placement in this fog system with three fog cells and formulated the problem as
a mixed integer linear programming (MILP) optimization model with the objective
of minimizing the networking and processing power consumption of the fog
system. The results indicate that in our proposed architecture, the processing
power consumption is the crucial element to achieve energy efficient VMs
placement.


title: Ginzburg-Landau Equations on Non-compact Riemann Surfaces

abstract:
  We construct nontrivial solutions to the magnetic Ginzburg-Landau equations
on non-compact Riemann surfaces with constant negative curvature. These
solutions are the non-commutative generalizations of the Abrikosov vortex
lattice in superconductivity. Our construction is done explicitly through a
bifurcation method. We obtain precise asymptotic expansions of these solutions
in terms of the curvature of the underlying Riemann surface, which serves as
the bifurcation parameter. For all results in this paper, we do not require
self-dualityfor the Ginzburg-Landau parameter.


title:
Truncated Beam Sweeping for Spatial Covariance Matrix Reconstruction in
  Hybrid Massive MIMO

abstract:
  Spatial covariance matrix (SCM) is essential in many applications of
multi-antenna systems such as massive multiple-input multiple-output (MIMO).
For massive MIMO operating at millimeter-wave bands, hybrid analog-digital
structure has been adopted to reduce the cost of radio frequency (RF) chains.
In this situation, signals received at the antennas are unavailable to the
digital receiver, and as a consequence, traditional sample average approach
cannot be used for SCM reconstruction in hybrid massive MIMO. To address this
issue, beam sweeping algorithm (BSA), which can reconstruct SCM effectively in
hybrid massive MIMO, has been proposed in our previous work. In this paper, a
truncated BSA is further proposed for SCM reconstruction by taking into account
the patterns of antenna elements in the array. Due to the directive antenna
pattern, sweeping results corresponding to predetermined direction-of-angles
(DOA) far from the normal direction are small and thus can be replaced by
predetermined constants. At the cost of negligible performance reduction, SCM
can be reconstructed efficiently by sweeping only the predetermined DOAs that
are close to the normal direction. In this way, BSA can be conducted much
faster than its traditional counterpart. Insightful analysis will be also
included to show the impact of truncation on the performance.


title: On a sharp inequality of Adimurthi-Druet type and extremal functions

abstract:
  Our main purpose in this paper is to establish the existence and nonexistence
of extremal functions for sharp inequality of Adimurthi-Druet type for
fractional dimensions on the entire space. Precisely, we extend the sharp
Trudinger-Moser type inequality in (Calc.Var.Partial Differential Equations,
\textbf{52} (2015) 125-163) for the entire space. In addition, we perform the
two-step strategy of Carleson-Chang together blow up analysis method to ensure
the existence of maximizers for the associated extremal problems for both
subcritical and critical regimes. We also present a nonexistence result under
subcritical regime for some special cases.


title:
Targeted Writing and Deleting of Magnetic Skyrmions in Two-Terminal
  Nanowire Devices

abstract:
  Controllable writing and deleting of nanoscale magnetic skyrmions are key
requirements for their use as information carriers for next-generation memory
and computing technologies. While several schemes have been proposed, they
require complex fabrication techniques or precisely tailored electrical inputs,
which limits their long-term scalability. Here we demonstrate an alternative
approach for writing and deleting skyrmions using conventional electrical
pulses within a simple, two-terminal wire geometry. X-ray microscopy
experiments and micromagnetic simulations establish the observed skyrmion
creation and annihilation as arising from Joule heating and Oersted field
effects of the current pulses, respectively. The unique characteristics of
these writing and deleting schemes, such as spatial and temporal selectivity,
together with the simplicity of the 2-terminal device architecture, provide a
flexible and scalable route to the viable applications of skyrmions.


title:
Detection of genuine tripartite entanglement based on Bloch
  representation of densitymatrices

abstract:
  We study the genuine multipartite entanglement in tripartite quantum systems.
By using the Schmidt decomposition and local unitary transformation, we convert
the general states to simpler forms and consider certain matrices from
correlation tensors in the Bloch representation of the simplified density
matrices. Using these special matrices, we obtain new criteria for genuine
multipartite entanglement. Detail examples show that our criteria are able to
detect more tripartite entangled and genuine tripartite entangled states than
some existing criteria.


title:
All the codeword bits in polar codes have the same BER under the SC
  decoder

abstract:
  We prove that for any binary-input memoryless symmetric (BMS) channel, any
code length, and any code dimension, all the codeword bits in polar codes have
the same bit error rate (BER) under the successive cancellation (SC) decoder.


title:
Identifying a minimal flavor symmetry of the seesaw mechanism behind
  neutrino oscillations

abstract:
  In the canonical seesaw framework flavor mixing and CP violation in weak
charged-current interactions of light and heavy Majorana neutrinos are
correlated with each other and described respectively by the $3\times 3$
matrices $U$ and $R$. We show that the very possibility of $\big|U^{}_{\mu
i}\big| = \big|U^{}_{\tau i}\big|$ (for $i = 1, 2, 3$), which is strongly
indicated by current neutrino oscillation data, automatically leads to a novel
prediction $\big|R^{}_{\mu i}\big| = \big|R^{}_{\tau i}\big|$ (for $i = 1, 2,
3$). We prove that behind these two sets of equalities and the experimental
evidence for leptonic CP violation lies a minimal flavor symmetry -- the
overall neutrino mass term keeps invariant when the left-handed neutrino fields
transform as $\nu^{}_{e \rm L} \to (\nu^{}_{e \rm L})^c$, $\nu^{}_{\mu \rm L}
\to (\nu^{}_{\tau \rm L})^c$, $\nu^{}_{\tau \rm L} \to (\nu^{}_{\mu \rm L})^c$
and the right-handed neutrino fields undergo an arbitrary unitary CP
transformation. Such a generalized $\mu$-$\tau$ reflection symmetry can greatly
help constrain the flavor textures of active and sterile neutrinos and hence
enhance predictability and testability of the seesaw mechanism.


title:
RSTT: Real-time Spatial Temporal Transformer for Space-Time Video
  Super-Resolution

abstract:
  Space-time video super-resolution (STVSR) is the task of interpolating videos
with both Low Frame Rate (LFR) and Low Resolution (LR) to produce
High-Frame-Rate (HFR) and also High-Resolution (HR) counterparts. The existing
methods based on Convolutional Neural Network~(CNN) succeed in achieving
visually satisfied results while suffer from slow inference speed due to their
heavy architectures. We propose to resolve this issue by using a
spatial-temporal transformer that naturally incorporates the spatial and
temporal super resolution modules into a single model. Unlike CNN-based
methods, we do not explicitly use separated building blocks for temporal
interpolations and spatial super-resolutions; instead, we only use a single
end-to-end transformer architecture. Specifically, a reusable dictionary is
built by encoders based on the input LFR and LR frames, which is then utilized
in the decoder part to synthesize the HFR and HR frames. Compared with the
state-of-the-art TMNet \cite{xu2021temporal}, our network is $60\%$ smaller
(4.5M vs 12.3M parameters) and $80\%$ faster (26.2fps vs 14.3fps on
$720\times576$ frames) without sacrificing much performance. The source code is
available at https://github.com/llmpass/RSTT.


title:
Educational Question Generation of Children Storybooks via Question Type
  Distribution Learning and Event-Centric Summarization

abstract:
  Generating educational questions of fairytales or storybooks is vital for
improving children's literacy ability. However, it is challenging to generate
questions that capture the interesting aspects of a fairytale story with
educational meaningfulness. In this paper, we propose a novel question
generation method that first learns the question type distribution of an input
story paragraph, and then summarizes salient events which can be used to
generate high-cognitive-demand questions. To train the event-centric
summarizer, we finetune a pre-trained transformer-based sequence-to-sequence
model using silver samples composed by educational question-answer pairs. On a
newly proposed educational question answering dataset FairytaleQA, we show good
performance of our method on both automatic and human evaluation metrics. Our
work indicates the necessity of decomposing question type distribution learning
and event-centric summary generation for educational question generation.


title:
mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary
  Research Collaborations

abstract:
  The growing amount of data and advances in data science have created a need
for a new kind of cloud platform that provides users with flexibility, strong
security, and the ability to couple with supercomputers and edge devices
through high-performance networks. We have built such a nation-wide cloud
platform, called "mdx" to meet this need. The mdx platform's virtualization
service, jointly operated by 9 national universities and 2 national research
institutes in Japan, launched in 2021, and more features are in development.
Currently mdx is used by researchers in a wide variety of domains, including
materials informatics, geo-spatial information science, life science,
astronomical science, economics, social science, and computer science. This
paper provides an the overview of the mdx platform, details the motivation for
its development, reports its current status, and outlines its future plans.


title:
New results and regression model for the exponentiated odd log-logistic
  Weibull family of distributions with applications

abstract:
  We obtain new mathematical properties of the exponentiated odd log-logistic
family of distributions, and of its special case named the exponentiated odd
log-logistic Weibull, and its log transformed. A new location and scale
regression model is constructed, and some simulations are carried out to verify
the behavior of the maximum likelihood estimators, and of the modified
deviance-based residuals. The methodology is applied to the Japanese-Brazilian
emigration data.


title: Deep Polarimetric HDR Reconstruction

abstract:
  This paper proposes a novel learning based high-dynamic-range (HDR)
reconstruction method using a polarization camera. We utilize a previous
observation that polarization filters with different orientations can attenuate
natural light differently, and we treat the multiple images acquired by the
polarization camera as a set acquired under different exposure times, to
introduce the development of solutions for the HDR reconstruction problem. We
propose a deep HDR reconstruction framework with a feature masking mechanism
that uses polarimetric cues available from the polarization camera, called Deep
Polarimetric HDR Reconstruction (DPHR). The proposed DPHR obtains polarimetric
information to propagate valid features through the network more effectively to
regress the missing pixels. We demonstrate through both qualitative and
quantitative evaluations that the proposed DPHR performs favorably than
state-of-the-art HDR reconstruction algorithms.


title:
Interpretable Machine Learning Models for Modal Split Prediction in
  Transportation Systems

abstract:
  Modal split prediction in transportation networks has the potential to
support network operators in managing traffic congestion and improving transit
service reliability. We focus on the problem of hourly prediction of the
fraction of travelers choosing one mode of transportation over another using
high-dimensional travel time data. We use logistic regression as base model and
employ various regularization techniques for variable selection to prevent
overfitting and resolve multicollinearity issues. Importantly, we interpret the
prediction accuracy results with respect to the inherent variability of modal
splits and travelers' aggregate responsiveness to changes in travel time. By
visualizing model parameters, we conclude that the subset of segments found
important for predictive accuracy changes from hour-to-hour and include
segments that are topologically central and/or highly congested. We apply our
approach to the San Francisco Bay Area freeway and rapid transit network and
demonstrate superior prediction accuracy and interpretability of our method
compared to pre-specified variable selection methods.


title:
Laser-induced electron Fresnel diffraction by XUV pulses at extreme
  intensity

abstract:
  Ionization of atoms and molecules in laser fields can lead to various
interesting interference structures in the photoelectron spectrum. For the case
of a super-intense extreme ultraviolet laser pulse, we identify a novel
petal-like interference structure in the electron momentum distribution along
the direction of the laser field propagation. We show that this structure is
quite general and can be attributed to the Fresnel diffraction of the
electronic wavepacket by the nucleus. Our results are demonstrated by
numerically solving the time-dependent Schrodinger equation of the atomic
hydrogen beyond the dipole approximation. By building an analytical model, we
find that the electron displacement determines the aforementioned interference
pattern. In addition, we establish the physical picture of laser-induced
electron Fresnel diffraction which is reinforced by both quantum and
semiclassical models.


title:
Giant bulk spin-orbit torque and efficient electrical switching in
  single ferrimagnetic FeTb layers with strong perpendicular magnetic
  anisotropy

abstract:
  Efficient manipulation of antiferromagnetically coupled materials that are
integration-friendly and have strong perpendicular magnetic anisotropy (PMA) is
of great interest for low-power, fast, dense magnetic storage and computing.
Here, we report a distinct, giant bulk damping-like spin-orbit torque in
strong-PMA ferrimagnetic Fe100-xTbx single layers that are integration-friendly
(composition-uniform, amorphous, sputter-deposited). For sufficiently-thick
layers, this bulk torque is constant in the efficiency per unit layer
thickness, {\xi}_DL^j/t, with a record-high value of 0.036nm-1, and the
dampinglike torque efficiency {\xi}_DL^j achieves very large values for thick
layers, up to 300% for 90 nm layers. This giant bulk torque by itself switches
tens of nm thick Fe100-xTbx layers that have very strong PMA and high
coercivity at current densities as low as a few MA/cm2. Surprisingly, for a
given layer thickness, {\xi}_DL^j shows strong composition dependence and
becomes negative for composition where the total angular momentum is oriented
parallel to the magnetization rather than antiparallel. Our findings of giant
bulk spin torque efficiency and intriguing torque-compensation correlation will
stimulate study of such unique spin-orbit phenomena in a variety of
ferrimagnetic hosts. This work paves a promising avenue for developing
ultralow-power, fast, dense ferrimagnetic storage and computing devices.


title: Free energy and inference in living systems

abstract:
  Organisms are nonequilibrium stationary systems self-organized from
spontaneous symmetry breaking in the environment and undergo irreversible work
cycles without detailed balance. The thermodynamic free-energy principle (FEP)
describes an organism's homeostasis as regulating metabolic work constrained by
the physical FE cost. In contrast, the recent neuroscience and theoretical
biology efforts account for an organism's adaptive fitness as purposeful
allostasis by demanding minimization of the informational FE. For a coalesced
view of life, this study proposes a physically-principled FE minimization
theory overarching the essential features from both FEPs. Consequently, the
ensuing Bayesian mechanics reveals that the brain function of perception and
behavioral control operates like Schr{\"o}dinger's clockwork and develops
optimal trajectories in neural manifolds when the sensory perturbation is
aroused. Furthermore, the sensory and motor interactions of the biological
agents with the environment are manifested to induce a dynamic transition
between neural attractors.


title:
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization
  Perspective

abstract:
  The lack of adversarial robustness has been recognized as an important issue
for state-of-the-art machine learning (ML) models, e.g., deep neural networks
(DNNs). Thereby, robustifying ML models against adversarial attacks is now a
major focus of research. However, nearly all existing defense methods,
particularly for robust training, made the white-box assumption that the
defender has the access to the details of an ML model (or its surrogate
alternatives if available), e.g., its architectures and parameters. Beyond
existing works, in this paper we aim to address the problem of black-box
defense: How to robustify a black-box model using just input queries and output
feedback? Such a problem arises in practical scenarios, where the owner of the
predictive model is reluctant to share model information in order to preserve
privacy. To this end, we propose a general notion of defensive operation that
can be applied to black-box models, and design it through the lens of denoised
smoothing (DS), a first-order (FO) certified defense technique. To allow the
design of merely using model queries, we further integrate DS with the
zeroth-order (gradient-free) optimization. However, a direct implementation of
zeroth-order (ZO) optimization suffers a high variance of gradient estimates,
and thus leads to ineffective defense. To tackle this problem, we next propose
to prepend an autoencoder (AE) to a given (black-box) model so that DS can be
trained using variance-reduced ZO optimization. We term the eventual defense as
ZO-AE-DS. In practice, we empirically show that ZO-AE- DS can achieve improved
accuracy, certified robustness, and query complexity over existing baselines.
And the effectiveness of our approach is justified under both image
classification and image reconstruction tasks. Codes are available at
https://github.com/damon-demon/Black-Box-Defense.


title: HINT: Hierarchical Neuron Concept Explainer

abstract:
  To interpret deep networks, one main approach is to associate neurons with
human-understandable concepts. However, existing methods often ignore the
inherent relationships of different concepts (e.g., dog and cat both belong to
animals), and thus lose the chance to explain neurons responsible for
higher-level concepts (e.g., animal). In this paper, we study hierarchical
concepts inspired by the hierarchical cognition process of human beings. To
this end, we propose HIerarchical Neuron concepT explainer (HINT) to
effectively build bidirectional associations between neurons and hierarchical
concepts in a low-cost and scalable manner. HINT enables us to systematically
and quantitatively study whether and how the implicit hierarchical
relationships of concepts are embedded into neurons, such as identifying
collaborative neurons responsible to one concept and multimodal neurons for
different concepts, at different semantic levels from concrete concepts (e.g.,
dog) to more abstract ones (e.g., animal). Finally, we verify the faithfulness
of the associations using Weakly Supervised Object Localization, and
demonstrate its applicability in various tasks such as discovering saliency
regions and explaining adversarial attacks. Code is available on
https://github.com/AntonotnaWang/HINT.


title: Long-Tailed Recognition via Weight Balancing

abstract:
  In the real open world, data tends to follow long-tailed class distributions,
motivating the well-studied long-tailed recognition (LTR) problem. Naive
training produces models that are biased toward common classes in terms of
higher accuracy. The key to addressing LTR is to balance various aspects
including data distribution, training losses, and gradients in learning. We
explore an orthogonal direction, weight balancing, motivated by the empirical
observation that the naively trained classifier has "artificially" larger
weights in norm for common classes (because there exists abundant data to train
them, unlike the rare classes). We investigate three techniques to balance
weights, L2-normalization, weight decay, and MaxNorm. We first point out that
L2-normalization "perfectly" balances per-class weights to be unit norm, but
such a hard constraint might prevent classes from learning better classifiers.
In contrast, weight decay penalizes larger weights more heavily and so learns
small balanced weights; the MaxNorm constraint encourages growing small weights
within a norm ball but caps all the weights by the radius. Our extensive study
shows that both help learn balanced weights and greatly improve the LTR
accuracy. Surprisingly, weight decay, although underexplored in LTR,
significantly improves over prior work. Therefore, we adopt a two-stage
training paradigm and propose a simple approach to LTR: (1) learning features
using the cross-entropy loss by tuning weight decay, and (2) learning
classifiers using class-balanced loss by tuning weight decay and MaxNorm. Our
approach achieves the state-of-the-art accuracy on five standard benchmarks,
serving as a future baseline for long-tailed recognition.


title: Fast Radio Bursts

abstract:
  The era of fast radio bursts (FRBs) was open in 2007, when a very bright
radio pulse of unknown origin was discovered occasionally in the archival data
of Parkes Telescope. Over the past fifteen years, this mysterious phenomenon
have caught substantial attention among the scientific community and become one
of the hottest topic in high-energy astrophysics. The total number of events
has a dramatic increase to a few hundred recently, benefiting from new
dedicated surveys and improved observational techniques. Our understanding of
these bursts has been undergoing a revolutionary growth with observational
breakthroughs announced consistently. In this chapter, we will give a
comprehensive introduction of FRBs, including the latest progress. Starting
from the basics, we will go through population study, inherent physical
mechanism, and all the way to the application in cosmology. Plenty of open
questions exist right now and there is more surprise to come in this active
young field.


title:
Laser-induced electron Fresnel diffraction in the tunneling and
  over-barrier ionization

abstract:
  The photoelectron momentum distribution in the strong-field ionization has a
variety of structures that reveal the complicated dynamics of this process.
Recently, we identified a low-energy interference structure in the case of a
super-intense extreme ultraviolet (XUV) laser pulse and attributed it to the
laser-induced electron Fresnel diffraction. This structure is determined by the
laser-induced electron displacement [Geng L et al. 2021 Phys. Rev. A 104(2)
L021102]. In the present work, we find that the Fresnel diffraction picture is
also present in the tunneling and over-barrier regime of ionization by short
pulses. However, the electron displacement is now induced by the electric field
component of the laser pulse, instead of by the magnetic field component in the
case of the superintense XUV pulse. After corresponding modifications to our
quantum and semiclassical models, we find the same physical mechanism of the
Fresnel diffraction governs the low-energy interference structures along the
laser polarization. The results predicted by the two models agree well with the
accurate results from the numerical solution to the time-dependent Schrodinger
equation.


title:
The XXL Survey XLV. Linking the ages of optically selected groups to
  their X-ray emission

abstract:
  We investigate the properties of 232 optical spectroscopically selected
groups from the Galaxy And Mass Assembly (GAMA) survey that overlap the XXL
X-ray cluster survey. X-ray aperture flux measurements combined with GAMA group
data provides the largest available sample of optical groups with detailed
galaxy membership information and consistently measured X-ray fluxes and upper
limits. 142 of these groups are divided into three subsets based on the
relative strength of X-ray and optical emission, and we see a trend in galaxy
properties between these subsets: X-ray overluminous groups contain a lower
fraction of both blue and star forming galaxies compared with X-ray
underluminous systems. X-ray overluminous groups also have a more dominant
central galaxy, with a magnitude gap between first and second ranked galaxies
on average 0.22 mag larger than in underluminous groups. The central galaxy in
overluminous groups also lies closer to the centre of the group. We examine a
number of other structural properties of our groups, such as axis ratio,
velocity dispersion, and group crossing time and find trends with X-ray
emission in some of these properties despite the high stochastic noise from the
limited number of group galaxies. We attribute the trends we see to the
evolutionary state of groups, with X-ray overluminous systems being more
dynamically evolved than underluminous groups. The X-ray overluminous groups
have had more time to develop a luminous intragroup medium, quench member
galaxies, and build the mass of the central galaxy through mergers compared to
underluminous groups. However, a minority of X-ray underluminous groups have
properties that suggest them to be dynamically mature. The lack of hot gas in
these systems cannot be accounted for by high star formation efficiency,
suggesting that high gas entropy resulting from feedback is the likely cause of
their weak X-ray emission.


title:
Cryopreservation of seeds of blue waterlily (Nymphaea caerulea) using
  glutathione adding plant vitrification solution, PVS+

abstract:
  Nymphaea caerulea is a valuable freshwater aquatic plant, not only because of
its ornamental value but also its extractions for chemical and medical uses. It
is necessary to store its seeds as backup resources. Cryopreservation is the
reliable, cost-effective method for long-term preservation of botanical genetic
banks, especially for recalcitrant plants. In this study, we demonstrated that
due to unable to tolerate desiccation and low-temperature, N. caerulea is
recalcitrant. Since viability was lost before 6 months storage, N. caerulea
seeds were not appropriate to long-term store by traditional storage method.
The only way to long-term store N. caerulea seeds is cryopreservation. However,
Plant Vitrification Solution 3 (PVS3; 50% w/v sucrose, 50% w/v glycerol), a
commonly used plant vitrification solution, was ineffective on N. caerulea
seeds cryopreservation. The maximum survival rate of cryopreserved seeds
treated by PVS3 was only 23%. Oxidative stress induced by reactive oxygen
species (ROS) accumulation within seeds was the reason to cause inefficiency of
PVS3 treated seeds cryopreservation. We developed a new plant vitrification
solution (PVS+), by adding glutathione (GSH) into PVS3. PVS+ rescued the
inefficiency by decreasing ROS accumulation and elevated survival rate of
cryopreserved seeds to 97%. Our results showed some inefficiencies of plant
tissue cryopreservation may be caused by ROS-induced oxidative stress.
Antioxidants can reduce ROS-induced oxidative stress and improve seeds survival
after cryopreservation. In conclusion, N. caerulea seeds were identified as
recalcitrant and successfully cryopreserved. Suppressing ROS accumulation
during cryopreservation may be a potential strategy to improve survival rate of
recalcitrant seeds after cryopreservation.


title: Karhunen-Lo\`eve expansion of Random Measures

abstract:
  We present an orthogonal expansion for real regular second-order finite
random measures over $\mathbb{R}^{d}$. Such expansion, which may be seen as a
Karhunen-Lo\`eve decomposition, consists in a series expansion of deterministic
real finite measures weighted by uncorrelated real random variables with
summable variances. The convergence of the series is in a
mean-square-$\mathcal{M}_{B}(\mathbb{R}^{d})^{*}$-weak$^{*}$ sense, with
$\mathcal{M}_{B}(\mathbb{R}^{d})$ being the space of bounded measurable
functions over $\mathbb{R}^{d}$. This is proven profiting the extra requirement
for a regular random measure that its covariance structure is identified with a
covariance measure over $\mathbb{R}^{d}\times\mathbb{R}^{d}$. We also obtain a
series decomposition of the covariance measure which converges in a separately
$\mathcal{M}_{B}(\mathbb{R}^{d})^{*}$-weak$^{*}-$total-variation sense. We then
obtain an analogous result for function-regulated regular random measures.


title:
A Survey of Super-Resolution in Iris Biometrics with Evaluation of
  Dictionary-Learning

abstract:
  The lack of resolution has a negative impact on the performance of
image-based biometrics. While many generic super-resolution methods have been
proposed to restore low-resolution images, they usually aim to enhance their
visual appearance. However, a visual enhancement of biometric images does not
necessarily correlate with a better recognition performance. Reconstruction
approaches need thus to incorporate specific information from the target
biometric modality to effectively improve recognition. This paper presents a
comprehensive survey of iris super-resolution approaches proposed in the
literature. We have also adapted an Eigen-patches reconstruction method based
on PCA Eigen-transformation of local image patches. The structure of the iris
is exploited by building a patch-position dependent dictionary. In addition,
image patches are restored separately, having their own reconstruction weights.
This allows the solution to be locally optimized, helping to preserve local
information. To evaluate the algorithm, we degraded high-resolution images from
the CASIA Interval V3 database. Different restorations were considered, with
15x15 pixels being the smallest resolution. To the best of our knowledge, this
is among the smallest resolutions employed in the literature. The framework is
complemented with six public iris comparators, which were used to carry out
biometric verification and identification experiments. Experimental results
show that the proposed method significantly outperforms both bilinear and
bicubic interpolation at very low-resolution. The performance of a number of
comparators attains an impressive Equal Error Rate as low as 5%, and a Top-1
accuracy of 77-84% when considering iris images of only 15x15 pixels. These
results clearly demonstrate the benefit of using trained super-resolution
techniques to improve the quality of iris images prior to matching.


title:
NLO QCD corrections to pseudoscalar quarkonium production with two heavy
  flavors in photon-photon collision

abstract:
  We calculate the next-to-leading order (NLO) quantum chromodynamics (QCD)
corrections to $\gamma+\gamma\to \eta_c+c+\bar{c}$, $\gamma+\gamma\to
\eta_b+b+\bar{b}$ and $\gamma+\gamma\to B_c+b+\bar{c}$ processes in the
framework of non-relativistic QCD (NRQCD) factorization formalism. The cross
sections at the SuperKEKB electron-positron collider, as well as the future
collider like the Circular Electron Positron Collider (CEPC), are evaluated.
Numerical results indicate that the NLO corrections are significant, and the
uncertainties in theoretical predictions with NLO corrections are reduced as
expected. Due to the high luminosity of the SuperKEKB collider, the
$\eta_c+c+\bar{c}$ production is hopefully observable in the near future.


title:
An Empirical Study and Comparison of Recent Few-Shot Object Detection
  Algorithms

abstract:
  The generic object detection (GOD) task has been successfully tackled by
recent deep neural networks, trained by an avalanche of annotated training
samples from some common classes. However, it is still non-trivial to
generalize these object detectors to the novel long-tailed object classes,
which has only few labeled training samples. To this end, the Few-Shot Object
Detection (FSOD) has been topical recently, as it mimics the humans' ability of
learning to learn, and intelligently transfers the learnt generic object
knowledge from the common heavy-tailed, to the novel long-tailed object
classes. Especially, the research in this emerging field has been flourish in
the recent years with various benchmarks, backbones, and methodologies
proposed. To review these FSOD works, there are several insightful FSOD survey
articles that systematically study and compare them as the groups of
fine-tuning/transfer learning, and meta-learning methods. In contrast, we
compare these FSOD algorithms from the new perspective and taxonomy of their
contributions, i.e., data-oriented, model-oriented, and algorithm oriented
ones. Thus, an empirical study and comparison has been conducted on the recent
achievements of FSOD. Furthermore, we also analyze the technical challenges,
the merits and demerits of these methods, and envision the future directions of
FSOD. Specifically, we give an overview of FSOD, including the problem
definition, common datasets, and evaluation protocols. A new taxonomy is then
proposed based on the role of prior knowledge during object detection of novel
classes. Following this taxonomy, we provide a systematic review of the
advances in FSOD. Finally, further discussions on performance, challenges, and
future directions are presented.


title:
Denoising Likelihood Score Matching for Conditional Score-based Data
  Generation

abstract:
  Many existing conditional score-based data generation methods utilize Bayes'
theorem to decompose the gradients of a log posterior density into a mixture of
scores. These methods facilitate the training procedure of conditional score
models, as a mixture of scores can be separately estimated using a score model
and a classifier. However, our analysis indicates that the training objectives
for the classifier in these methods may lead to a serious score mismatch issue,
which corresponds to the situation that the estimated scores deviate from the
true ones. Such an issue causes the samples to be misled by the deviated scores
during the diffusion process, resulting in a degraded sampling quality. To
resolve it, we formulate a novel training objective, called Denoising
Likelihood Score Matching (DLSM) loss, for the classifier to match the
gradients of the true log likelihood density. Our experimental evidence shows
that the proposed method outperforms the previous methods on both Cifar-10 and
Cifar-100 benchmarks noticeably in terms of several key evaluation metrics. We
thus conclude that, by adopting DLSM, the conditional scores can be accurately
modeled, and the effect of the score mismatch issue is alleviated.


title: Rebuild and Ensemble: Exploring Defense Against Text Adversaries

abstract:
  Adversarial attacks can mislead strong neural models; as such, in NLP tasks,
substitution-based attacks are difficult to defend. Current defense methods
usually assume that the substitution candidates are accessible, which cannot be
widely applied against adversarial attacks unless knowing the mechanism of the
attacks. In this paper, we propose a \textbf{Rebuild and Ensemble} Framework to
defend against adversarial attacks in texts without knowing the candidates. We
propose a rebuild mechanism to train a robust model and ensemble the rebuilt
texts during inference to achieve good adversarial defense results. Experiments
show that our method can improve accuracy under the current strong attack
methods.


title:
Towards Discriminative Representation: Multi-view Trajectory Contrastive
  Learning for Online Multi-object Tracking

abstract:
  Discriminative representation is crucial for the association step in
multi-object tracking. Recent work mainly utilizes features in single or
neighboring frames for constructing metric loss and empowering networks to
extract representation of targets. Although this strategy is effective, it
fails to fully exploit the information contained in a whole trajectory. To this
end, we propose a strategy, namely multi-view trajectory contrastive learning,
in which each trajectory is represented as a center vector. By maintaining all
the vectors in a dynamically updated memory bank, a trajectory-level
contrastive loss is devised to explore the inter-frame information in the whole
trajectories. Besides, in this strategy, each target is represented as multiple
adaptively selected keypoints rather than a pre-defined anchor or center. This
design allows the network to generate richer representation from multiple views
of the same target, which can better characterize occluded objects.
Additionally, in the inference stage, a similarity-guided feature fusion
strategy is developed for further boosting the quality of the trajectory
representation. Extensive experiments have been conducted on MOTChallenge to
verify the effectiveness of the proposed techniques. The experimental results
indicate that our method has surpassed preceding trackers and established new
state-of-the-art performance.


title:
Phase transitions and resilience of the MDCDW phase at finite
  temperature and density

abstract:
  We study the phase transitions of the magnetic dual chiral density wave
(MDCDW). This spatially inhomogeneous phase emerges in cold, dense QCD in the
presence of a strong magnetic field. Starting from the generalized GL expansion
of the free energy, we derive several analytical formulas that enable fast
numerical computation of the expansion coefficients to arbitrary order,
allowing high levels of precision in the determination of the physical
dynamical parameters, as well as in the transition curves in the temperature
vs. chemical potential plane at different magnetic fields. At magnetic fields
and temperatures compatible with neutron star (NS) conditions, the MDCDW
remains favored over the symmetric ground state at all densities. The phase's
"resilience" manifests in (1) a region of small but nonzero remnant mass and
significant modulation at intermediate densities, originating in part from the
nontrivial topology of the lowest Landau level, and (2) a region of increasing
condensate parameters at high densities. Our analysis suggests the MDCDW
condensate remains energetically favored at densities and temperatures much
higher than previously considered, opening the possibility for this phase to be
a viable candidate for the matter structure of even young neutron stars
produced by NS mergers.


title: Quantum annealing with pairs of $^2\Sigma$ molecules as qubits

abstract:
  The unique rotational and fine structure of $^2\Sigma$ molecules gives rise
to crossings between Zeeman states of different parity. These crossings become
avoided in the presence of an electric field. We propose an algorithm that
encodes Ising models into qubits defined by pairs of $^2\Sigma$ molecules
sharing an excitation near these avoided crossings. This can be used to realize
a transverse field Ising model tunable by an external electric or magnetic
field, suitable for quantum annealing applications. We perform dynamical
calculations for several examples with 1D and 2D connectivities. Our results
demonstrate that the probability of obtaining valid annealing solutions is high
and can be optimized by varying the annealing times.


title:
DepthFormer: Exploiting Long-Range Correlation and Local Information for
  Accurate Monocular Depth Estimation

abstract:
  This paper aims to address the problem of supervised monocular depth
estimation. We start with a meticulous pilot study to demonstrate that the
long-range correlation is essential for accurate depth estimation. Therefore,
we propose to leverage the Transformer to model this global context with an
effective attention mechanism. We also adopt an additional convolution branch
to preserve the local information as the Transformer lacks the spatial
inductive bias in modeling such contents. However, independent branches lead to
a shortage of connections between features. To bridge this gap, we design a
hierarchical aggregation and heterogeneous interaction module to enhance the
Transformer features via element-wise interaction and model the affinity
between the Transformer and the CNN features in a set-to-set translation
manner. Due to the unbearable memory cost caused by global attention on
high-resolution feature maps, we introduce the deformable scheme to reduce the
complexity. Extensive experiments on the KITTI, NYU, and SUN RGB-D datasets
demonstrate that our proposed model, termed DepthFormer, surpasses
state-of-the-art monocular depth estimation methods with prominent margins.
Notably, it achieves the most competitive result on the highly competitive
KITTI depth estimation benchmark. Our codes and models are available at
https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox.


title: Penrose limits in massive type-IIA AdS$_3$ background

abstract:
  In this paper we consider the non-Abelian T-dual geometry of the type $IIB$
supergravity theory on $AdS_3\times S^3\times T^4$ background along a
convenient $SU(2)$ subgroup of the $SO(4)$ R-symmetry. We examine various null
geodesics of the resulting massive type $IIA$ supergravity theory and
investigate the Penrose limits along these geodesics. We find that one of the
resulting backgrounds admits pp-wave geometry in the neighbourhood of a
suitable null geodesic. We carry out the supersymmetry analysis of the
resulting pp-wave geometry and observe that it preserves sixteen supercharges.
Further we comment on the possible gauge theory dual of the resulting pp-wave
background.


title:
Exact Results for the Tavis-Cummings and H$\boldsymbol{\ddot{u}}$ckel
  Hamiltonians with Diagonal Disorder

abstract:
  We present an exact method to calculate the electronic states of one electron
Hamiltonians with diagonal disorder. We show that the disorder averaged one
particle Green's function can be calculated directly, using a deterministic
complex (non-Hermitian) Hamiltonian. For this, we assume that the molecular
states have a Cauchy (Lorentz) distribution and use the supersymmetric method
which has already been used in problems of solid state physics. Using the
method we find exact solutions to the states of $N$ molecules, confined to a
microcavity, for any value of $N$. Our analysis shows that the width of the
polaritonic states as a function of $N$ depend on the nature of disorder, and
hence can be used to probe the way molecular energy levels are distributed. We
also show how one can find exact results for H$\ddot{u}$ckel type Hamiltonians
with on-site, Cauchy disorder and demonstrate its use.


title: Enhancing Speckle Statistics for Imaging inside Scattering Media

abstract:
  We exploit memory effect speckle correlations for the imaging of incoherent
linear (single-photon) fluorescent sources behind scattering tissue. While
memory effect-based imaging techniques have been heavily studied in the past,
for thick scattering layers and complex illumination patterns these
correlations are weak, limiting the practice applicability of the idea. In this
work, we introduce a Spatial Light Modulator (SLM) between the tissue sample
and the imaging sensor and capture multiple modulations of the speckle pattern.
We show that by correctly designing the modulation pattern and the
reconstruction algorithm we can greatly enhance statistical correlations in the
data. We exploit this to demonstrate the reconstruction of mega-pixel wide
fluorescent patterns behind scattering tissue.


title: Knowledge Mining with Scene Text for Fine-Grained Recognition

abstract:
  Recently, the semantics of scene text has been proven to be essential in
fine-grained image classification. However, the existing methods mainly exploit
the literal meaning of scene text for fine-grained recognition, which might be
irrelevant when it is not significantly related to objects/scenes. We propose
an end-to-end trainable network that mines implicit contextual knowledge behind
scene text image and enhance the semantics and correlation to fine-tune the
image representation. Unlike the existing methods, our model integrates three
modalities: visual feature extraction, text semantics extraction, and
correlating background knowledge to fine-grained image classification.
Specifically, we employ KnowBert to retrieve relevant knowledge for semantic
representation and combine it with image features for fine-grained
classification. Experiments on two benchmark datasets, Con-Text, and Drink
Bottle, show that our method outperforms the state-of-the-art by 3.72\% mAP and
5.39\% mAP, respectively. To further validate the effectiveness of the proposed
method, we create a new dataset on crowd activity recognition for the
evaluation. The source code and new dataset of this work are available at
https://github.com/lanfeng4659/KnowledgeMiningWithSceneText.


title:
Efficient and Degradation-Adaptive Network for Real-World Image
  Super-Resolution

abstract:
  Efficient and effective real-world image super-resolution (Real-ISR) is a
challenging task due to the unknown complex degradation of real-world images
and the limited computation resources in practical applications. Recent
research on Real-ISR has achieved significant progress by modeling the image
degradation space; however, these methods largely rely on heavy backbone
networks and they are inflexible to handle images of different degradation
levels. In this paper, we propose an efficient and effective
degradation-adaptive super-resolution (DASR) network, whose parameters are
adaptively specified by estimating the degradation of each input image.
Specifically, a tiny regression network is employed to predict the degradation
parameters of the input image, while several convolutional experts with the
same topology are jointly optimized to specify the network parameters via a
non-linear mixture of experts. The joint optimization of multiple experts and
the degradation-adaptive pipeline significantly extend the model capacity to
handle degradations of various levels, while the inference remains efficient
since only one adaptively specified network is used for super-resolving the
input image. Our extensive experiments demonstrate that the proposed DASR is
not only much more effective than existing methods on handling real-world
images with different degradation levels but also efficient for easy
deployment. Codes, models and datasets are available at
https://github.com/csjliang/DASR.