title:
Ideal-observer computation with anthropomorphic phantoms using Markov
  Chain Monte Carlo

abstract:
  In medical imaging, it is widely recognized that image quality should be
objectively evaluated based on performance in clinical tasks. To evaluate
performance in signal-detection tasks, the ideal observer (IO) is optimal but
also challenging to compute in clinically realistic settings. Markov Chain
Monte Carlo (MCMC)-based strategies have demonstrated the ability to compute
the IO using pre-computed projections of an anatomical database. To evaluate
image quality in clinically realistic scenarios, the observer performance
should be measured for realistic patient distribution. This implies that the
anatomical database should also be derived from a realistic population. In this
manuscript, we propose to advance the MCMC-based approach to achieve these
goals. We then use the proposed approach to study the effect of anatomical
database size on IO computation for the task of detecting perfusion defects in
simulated myocardial perfusion SPECT images. Our preliminary results provide
evidence that the size of the anatomical database affects the computation of
IO.


title: Quantitative estimates for the Bakry-Ledoux isoperimetric inequality. II

abstract:
  Concerning quantitative isoperimetry for a weighted Riemannian manifold
satisfying $\mathrm{Ric}_{\infty} \ge 1$, we give an $L^1$-estimate exhibiting
that the push-forward of the reference measure by the guiding function (arising
from the needle decomposition) is close to the Gaussian measure. We also show
$L^p$- and $W_2$-estimates in the $1$-dimensional case.


title:
Multi-scale Investigation of Chemical Short-Range Order and Dislocation
  Glide in the MoNbTi and TaNbTi Refractory Multi-Principal Element Alloys

abstract:
  Refractory multi-principal element alloys (RMPEAs) are promising materials
for high-temperature structural applications. Here, we investigate the role of
chemical short-range ordering (CSRO) on dislocation glide in two model RMPEAs -
TaNbTi and MoNbTi - using a multi-scale modeling approach. A highly accurate
machine learning interatomic potential was developed for the Mo-Ta-Nb-Ti system
and used to demonstrate that MoNbTi exhibits a much greater degree of SRO than
TaNbTi and the local composition has a direct effect on the unstable stacking
fault energies (USFE). From mesoscale phase-field dislocation dynamics
simulations, we find that increasing SRO leads to higher mean USFEs, thereby
increasing the stress required for dislocation glide. The gliding dislocations
experience significant hardening due to pinning and depinning caused by random
compositional fluctuations, with higher SRO decreasing the degree of USFE
dispersion and hence, amount of hardening. Finally, we show how the morphology
of an expanding dislocation loop is affected by the applied stress, with higher
SRO requiring higher applied stresses to achieve smooth screw dislocation
glide.


title:
CrowdFormer: Weakly-supervised Crowd counting with Improved
  Generalizability

abstract:
  Convolutional neural networks (CNNs) have dominated the field of computer
vision for nearly a decade due to their strong ability to learn local features.
However, due to their limited receptive field, CNNs fail to model the global
context. On the other hand, transformer, an attention-based architecture can
model the global context easily. Despite this, there are limited studies that
investigate the effectiveness of transformers in crowd counting. In addition,
the majority of the existing crowd counting methods are based on the regression
of density maps which requires point-level annotation of each person present in
the scene. This annotation task is laborious and also error-prone. This has led
to increased focus on weakly-supervised crowd counting methods which require
only the count-level annotations. In this paper, we propose a weakly-supervised
method for crowd counting using a pyramid vision transformer. We have conducted
extensive evaluations to validate the effectiveness of the proposed method. Our
method is comparable to the state-of-the-art on the benchmark crowd datasets.
More importantly, it shows remarkable generalizability.


title: White-dwarf asteroseismology with the {\sl TESS} space telescope

abstract:
  As white-dwarf (WD) stars cool, they go through one or more stages of
$g$(gravity)-mode pulsational instability, becoming multiperiodic variable
stars. Stars passing through these instability domains allow astronomers to
study their interiors through asteroseismological techniques, as if they could
"look" at their interiors by analyzing the spectra of pulsation periods. WD
asteroseismology has experienced extraordinary advances in recent years thanks
to photometric observations of unprecedented quality delivered by space
missions such as Kepler and TESS. These advances in the monitoring of variable
WDs have been accompanied by the development of new stellar models and
techniques for modeling their pulsations. In this article, we review the most
outstanding findings -- up to early 2022 -- about these fascinating pulsating
stars made possible with the high-sensitivity and continuous observations of
the ongoing TESS mission, bearing in mind that there will possibly be many more
new results in the immediate future derived from this unique space telescope.


title: Barriers to formation of traversable wormholes

abstract:
  Dynamical wormhole solutions in spherical symmetry cannot have
Ellis--Morris--Thorne or Simpson--Visser metrics as their static limit.
Possible static limits are not traversable and violate the quantum energy
inequality that bounds violations of the null energy condition, proscribing
them in semiclassical gravity.


title:
Static Prediction of Runtime Errors by Learning to Execute Programs with
  External Resource Descriptions

abstract:
  The execution behavior of a program often depends on external resources, such
as program inputs or file contents, and so cannot be run in isolation.
Nevertheless, software developers benefit from fast iteration loops where
automated tools identify errors as early as possible, even before programs can
be compiled and run. This presents an interesting machine learning challenge:
can we predict runtime errors in a "static" setting, where program execution is
not possible? Here, we introduce a real-world dataset and task for predicting
runtime errors, which we show is difficult for generic models like
Transformers. We approach this task by developing an interpreter-inspired
architecture with an inductive bias towards mimicking program executions, which
models exception handling and "learns to execute" descriptions of the contents
of external resources. Surprisingly, we show that the model can also predict
the location of the error, despite being trained only on labels indicating the
presence/absence and kind of error. In total, we present a practical and
difficult-yet-approachable challenge problem related to learning program
execution and we demonstrate promising new capabilities of interpreter-inspired
machine learning models for code.


title: The product structure of squaregraphs

abstract:
  A squaregraph is a plane graph in which each internal face is a $4$-cycle and
each internal vertex has degree at least 4. This paper proves that every
squaregraph is isomorphic to a subgraph of the semi-strong product of an
outerplanar graph and a path. We generalise this result for infinite
squaregraphs, and show that this is best possible in the sense that
"outerplanar graph" cannot be replaced by "forest".


title: A modelling framework for the analysis of the transmission of SARS-CoV2

abstract:
  Action plans against the current SARS-CoV-2 pandemic have been implemented
around the globe in order to reduce transmission. The reproduction number has
been found to respond to public health interventions changing throughout the
pandemic waves. However, the actual global burden of SARS-CoV-2 remains unknown
due to severe under-ascertainment of cases. The use of reported deaths has been
pointed out as a more reliable source of information, likely less prone to
under-reporting. Given that daily deaths occur from past infections weighted by
their probability of death, one may infer the true number of infections
accounting for their age distribution, using the data on reported deaths. We
adopt this framework and assume that the transmission dynamics generating the
total number of underlying infections can be described by a continuous time
transmission model expressed through a system of non-linear ordinary
differential equations, where the transmission rate and consequently the
reproduction number are stochastic. We model the transmission rate as a
diffusion process, with distinct volatility phases for each pandemic wave,
allowing to reveal both the effect of control strategies and the changes in
individuals behavior. We study the case of 6 European countries and estimate
the time-varying reproduction number ($R_t$) as well as the true cumulative
number of infected individuals using Stan's No-U-Turn sampler variant of
Hamiltonian Monte Carlo. As we estimate the true number of infections through
deaths, we offer a more accurate estimate of $R_t$. We also provide an estimate
of the daily reporting ratio and discuss the effects of changes in mobility and
testing to the inferred quantities.


title:
Exploring Physical-Based Constraints in Short-Term Load Forecasting: A
  Defense Mechanism Against Cyberattack

abstract:
  Short-term load forecasting is an essential task that supports utilities to
schedule generating sufficient power for balancing supply and demand, and can
become an attractive target for cyber attacks. It has been shown that the power
system state estimation is vulnerable to false data injection attacks.
Similarly, false data injection on input variables can result in large forecast
errors. The load forecasting system should have a protective mechanism to
mitigate such attacks. One approach is to model physical system constraints
that would identify anomalies. This study investigates possible constraints
associated with a load forecasting application. Looking at regional forecasted
loads, we analyze the relation between each zone through similarity measures
used in time series in order to identify constraints. Comprehensive results for
historical ERCOT load data indicate variation in the measures recognizing the
existence of malicious action. Still, these static measures can not be
considered an efficient index across different scenarios.


title: Discrete honeycombs, rational edges and edge states

abstract:
  Consider the tight binding model of graphene, sharply terminated along an
edge ${\bf l}$ parallel to a direction of translational symmetry of the
underlying period lattice. We classify such edges ${\bf l}$ into those of
"zigzag type" and those of "armchair type", generalizing the classical zigzag
and armchair edges. We prove that zero energy/flat band edge states arise for
edges of zigzag type, but never for those of armchair type. We exhibit explicit
formulas for flat band edge states when they exist. We produce strong evidence
for the existence of dispersive (non flat) edge state curves of nonzero energy
for most ${\bf l}$.

categories: math-ph cond-mat.mes-hall math.AP math.MP


title: Zero-delay Consistent and Smooth Trainable Interpolation

abstract:
  The question of how to produce a smooth interpolating curve from a stream of
data points is addressed in this paper. To this end, we formalize the concept
of real-time interpolator (RTI): a trainable unit that recovers smooth signals
that are consistent with the received input samples in an online manner.
Specifically, an RTI works under the requirement of producing a function
section immediately after a sample is received (zero delay), without changing
the reconstructed signal in past time sections. This work formulates the design
of spline-based RTIs as a bi-level optimization problem. Their training
consists in minimizing the average curvature of the interpolated signals over a
set of example sequences. The latter are representative of the nature of the
data sequence to be interpolated, allowing to tailor the RTI to a specific
signal source. Our overall design allows for different possible schemes. In
this work, we present two approaches, namely, the parametrized RTI and the
recurrent neural network (RNN)-based RTI, including their architecture and
properties. Experimental results show that the two proposed RTIs can be trained
in a data-driven fashion to achieve improved performance (in terms of the
curvature loss metric) with respect to a myopic-type RTI that only exploits the
local information at each time sample, while maintaining smooth, zero-delay,
and consistency requirements.


title: A class of Bernstein-type operators on the unit disk

abstract:
  We construct and study sequences of linear operators of Bernstein-type acting
on bivariate functions defined on the unit disk. To this end, we study
Bernstein-type operators under a domain transformation, we analyse the
bivariate Bernstein-Stancu operators, and we introduce Bernstein-type operators
on disk quadrants by means of continuously differentiable transformations of
the function. We state convergence results for continuous functions and we
estimate the rate of convergence. Finally some interesting numerical examples
are given, comparing approximations using the shifted Bernstein-Stancu and the
Bernstein-type operator on disk quadrants.


title:
A DG/CR discretization for the variational phase-field approach to
  fracture

abstract:
  Variational phase-field models of fracture are widely used to simulate
nucleation and propagation of cracks in brittle materials. They are based on
the approximation of the solutions of free-discontinuity fracture energy by two
smooth function: a displacement and a damage field. Their numerical
implementation is typically based on the discretization of both fields by nodal
P^1 Lagrange finite elements. In this article, we propose a nonconforming
approximation by discontinuous elements for the displacement and nonconforming
elements, whose gradient is more isotropic, for the damage. The handling of the
nonconformity is derived from that of heterogeneous diffusion problems. We
illustrate the robustness and versatility of the proposed method through series
of numerical examples.


title: Electronic effects on the radiation damage in high-entropy alloys

abstract:
  High-entropy alloys (HEAs) are exceptional candidates for radiation-resistant
materials due to their complex local chemical environment and slow defect
migration. Despite commonly overlooked, electronic effects on defects evolution
in radiation environments also play a crucial role by dissipating excess energy
through electron-phonon coupling and electronic heat conduction during cascade
events. We present a systematic study on electronic properties in random-solid
solutions (RSS) in four and five principal elements HEAs and their effect on
defect formation, clustering, and recombination. Electronic properties,
including electron-phonon coupling factor, the electronic specific heat, and
the electronic thermal conductivity, are computed within first-principles
calculations. Using the two-temperature molecular dynamics simulations, we show
that the electron-phonon coupling factor and electronic specific heat play a
critical role in Frenkel pairs formation. Specifically, the electron-phonon
coupling factor quickly dissipates the kinetic energy during primary knock-on
atom events via plasmon excitations and is subsequently dissipated via the
free-electrons conduction. We show that these effects are more critical than
the elastic distortion effects produced by the atomic mismatch. Of tremendous
interest, we show that including lighter elements helps to increase the
electron-phonon coupling factor, suggesting the possibility to improve
radiation resistance in HEA through optimal composition.


title: Norm operator method for boson expansions

abstract:
  We propose a new boson expansion theory that reveals and solves the problems
of the conventional boson expansion methods that have tried to elucidate
nuclear collective motion. The limitation of the number of phonon excitations
in addition to phonon excitation modes results in proper boson expansions and
allows the use of ideal boson state vectors. The norm operator plays a crucial
role and makes it possible to perform boson expansions without the
closed-algebra approximation. The treatment of the norm operator determines
whether the type of mapping is Hermitian or not, while the boson expansions
become infinite expansions regardless of the types of mapping. The
closed-algebra approximation makes small parameter expansions impossible and
the ideal boson state vectors, having no effect of the Pauli exclusion
principle, contain spurious components.


title:
Double-Sided Beamforming in OWC Systems Using Omni-Digital
  Reconfigurable Intelligent Surfaces

abstract:
  In this paper, we introduce a variant of reconfigurable intelligent surfaces
(RISs) called omni-digital-RISs (DRISs), which allow multiple physical
processes, with application to optical wireless communications systems. The
proposed omni-DRIS contains both reflectors and refractive elements, as well as
elements that perform both simultaneously. We describe and explain the concept
of omni-DRIS, suggest and analyze an omni-DRIS coding structure, discuss
metamaterials to be used, and provide a design example. Furthermore, we
demonstrate that the achievable rate of an omni-DRIS system depends on the
number of omni-DRIS elements, bits per phase shift, and the number of unused
elements. In addition, we show that the achievable rate upper bound is related
to the number of omni-DRIS elements, and conclude by discussing future research
directions.


title: Curved domain-wall fermions

abstract:
  We consider fermion systems on a square lattice with a mass term having a
curved domain-wall. Similarly to the conventional flat domain-wall fermions,
massless and chiral edge states appear on the wall. In the cases of $S^1$ and
$S^2$ domain-walls embedded into flat hypercubic lattices, we find that these
edge modes feel gravity through the induced Spin or Spin$^c$ connections. The
gravitational effect is encoded in the Dirac eigenvalue spectrum as a gap from
zero. In the standard continuum extrapolation of the square lattice, we find a
good agreement with the analytic prediction in the continuum theory. We also
find that the rotational symmetry of the edge modes is automatically recovered
in the continuum limit.

categories: hep-lat cond-mat.mes-hall hep-th math-ph math.MP


title: On the definition of stable transfer factors

abstract:
  We define stable geometric and spectral transfer factors and develop some of
their basic properties. Using our definition of stable geometric transfer
factors, we show that the stable transfer conjecture for orbital integrals
implies the stable transfer of characters and vice versa. The latter is also
implied by a basic form of the local Langlands conjecture, and in particular
establishes stable transfer in the archimedean case. Moreover, we introduce a
notion of primitive distributions and formulate conjectural transfer identities
for the local geometric and spectral distributions that occur in the stable
trace formula, suggested by primitisation.


title:
Scale Separated Approaches to the Interaction of Oceanic Internal Waves,
  part I: Theory

abstract:
  We report two parallel derivations for transport equations describing the
refraction of high frequency internal waves in a sea of random inertial waves.
One derivation casts the fields as amplitude modulated waves in an Eulerian
format and ultimately results in a Fokker-Planck (generalized diffusion)
equation. The second casts the high frequency waves as a frequency modulated
system. An ensemble averaged transport equation is obtained here that is
characterized by a dispersion of wave-packets about a mean drift in the
spectral domain. In this derivation the Fokker-Planck equation contains an
advective term. Both transport equations make unrealistic predictions for
energy sourced to internal wave breaking. The amplitude modulated system
predicts no mixing, the frequency modulated system over predicts by an order of
magnitude.


title:
The Circumstellar Environments of Double-Peaked, Calcium-strong
  Supernovae 2021gno and 2021inl

abstract:
  We present panchromatic observations and modeling of calcium-strong
supernovae (SNe) 2021gno in the star-forming host galaxy NGC 4165 (D = 30.5
Mpc) and 2021inl in the outskirts of elliptical galaxy NGC 4923 (D = 80 Mpc),
both monitored through the Young Supernova Experiment (YSE) transient survey.
The multi-color light curves of both SNe show two peaks, the former peak being
derived from shock cooling emission (SCE) and/or shock interaction with
circumstellar material (CSM). The primary peak in SN 2021gno is coincident with
luminous, rapidly decaying X-ray emission ($L_x = 5 \times 10^{41}$ erg
s$^{-1}$) detected by Swift-XRT at $\delta t = 1$ day after explosion, this
observation being the second ever detection of X-rays from a calcium-strong
transient. We interpret the X-ray emission from SN 2021gno in the context of
shock interaction with dense CSM that extends to $r < 3 \times 10^{14}$ cm.
Based on modeling of the SN 2021gno X-ray spectrum, we calculate a CSM mass
range of $M_{\rm CSM} = (0.3 - 1.6) \times 10^{-3}$ M$_{\odot}$ and particle
densities of $n = (1-4) \times 10^{10}$ cm$^{-3}$. Radio non-detections of SN
2021gno indicate a low-density environment at larger radii ($r > 10^{16}$ cm)
and a progenitor mass loss rate of $\dot{M} < 10^{-4}$ M$_{\odot}$ yr$^{-1}$,
for $v_w = 500$ km s$^{-1}$. For radiation derived from SCE, modeling of the
primary light curve peak in both SNe indicates an extended progenitor envelope
mass and radius of $M_e = 0.02 - 0.05$ M$_{\odot}$ and $R_e = 30 - 230$
R$_{\odot}$. The explosion properties of SNe 2021gno and 2021inl suggest
progenitor systems containing either a low-mass massive star or a white dwarf
(WD), the former being unlikely for either object given the lack of star
formation at both explosion sites. Furthermore, the progenitor environments of
both SNe are consistent with explosion models for low-mass hybrid He/C/O WD +
C/O WD binaries.


title: Locobatic theorem for disordered media and validity of linear response

abstract:
  Spectral localization is intrinsically unstable under perturbation. As a
result, the adiabatic theorem of quantum mechanics cannot generally hold for
localized eigenstates. However, it turns out that a remnant of the adiabatic
theorem, which we name the "locobatic theorem", survives: The physical
evolution of a typical eigenstate $\psi$ for a random system remains close,
with high probability, to the spectral flow for $\psi$ associated with a
restriction of the full Hamiltonian to a region where $\psi$ is supported. We
make the above statement precise for a class of Hamiltonians describing a
particle in a disordered background. Our argument relies on finding a local
structure that remains stable under the small perturbation of a random system.
  An application of this work is the justification of the linear response
formula for the Hall conductivity of a two-dimensional system with the Fermi
energy lying in a mobility gap. Additional results are concerned with
eigenvector hybridization in a one-dimensional Anderson model and the
construction of a Wannier basis for underlying spectral projections.


title:
Effect of Channel Geometry and Flow Rates in Hydrodynamic Focusing on
  Impedance Detection of Circulating Tumor Cells

abstract:
  Cells, other than their biological properties, have different electric and
physical properties. In an impedance cytometer, cells should pass one by one in
the detection region where pairs of electrodes are located. When cells are
located between electrodes, the impedance changes, and this can be indicative
of the presence of a cell. This is basically because the electric properties of
cells are different from the medium between the electrodes which is important
in determining the impedance. One of the most important aspects which influence
the performance of an impedance cytometer performance is the microchannel
design. In this work, in the first step, the microchannel was designed in a way
to have the best detection in the impedance cytometer. In this regard,
hydrodynamic focusing was selected to focus the population of cells entering
from the inlet of the main channel. To find the optimal parameters of the
microchannel, different geometry for the channel itself, along with flow rates
and other parameters related to sheath flow were simulated. In the next step,
impedance was measured in COMSOL for White blood cells, MCF7, and MDA-MB-231
breast cancer cells. The results show that by measuring the impedance of cells
using the optimized channel design, CTCs can be successfully differentiated
from WBCs.


title:
Time regularity of stochastic convolutions and stochastic evolution
  equations in duals of nuclear spaces

abstract:
  Let $\Phi$ a locally convex space and $\Psi$ be a quasi-complete,
bornological, nuclear space (like spaces of smooth functions and distributions)
with dual spaces $\Phi'$ and $\Psi'$. In this work we introduce sufficient
conditions for time regularity properties of the $\Psi'$-valued stochastic
convolution $\int_{0}^{t} \int_{U} S(t-r)'R(r,u) M(dr,du)$, $t \in [0,T]$,
where $(S(t): t \geq 0)$ is a $C_{0}$-semigroup on $\Psi$, $R(r,\omega,u)$ is a
suitable operator form $\Phi'$ into $\Psi'$ and $M$ is a cylindrical-martingale
valued measure on $\Phi'$. Our result is latter applied to study time
regularity of solutions to $\Psi'$-valued stochastic evolutions equations.


title:
The 2175 \AA~ Bump Features in FeLoBAL Quasars: One Indicator of MW-like
  Dust in the Nuclear Region of Quasar

abstract:
  To investigate the properties of dust in the nuclear region of quasars, we
explored the extinction curves of the iron low-ionization broad absorption line
(FeLoBAL) quasar SDSS J163004.29+311957.6 and its two analogues. The
parameterized extinction curves indicated the Milky Way-like 2175 \AA~ bump
features in underlying extinction, which are similar to those seen in the Local
Group and a subset of high-redshift star-forming galaxies. Compared to the bump
features in the Large Magellanic Clouds (LMC), the detections in this work are
much closer to those in the Milky Way (MW). These bump features, as well as
those in the high- and low-ionization broad absorption line (BAL) quasars of
Zhang et al., are probably the counterpart of the 2175 \AA~ bump features in
the quasar environment. This type of dust grain is generally small, easily
disrupted by high-energy photons and has difficulty surviving in the radiation
field of the active galactic nucleus (AGN). However, due to the presence of
absorption-line outflows, the 2175 \AA~ bump feature in quasars, which should
be rare, is seen many times in BAL quasars. The shielding effect of outflow
clouds allows the MW-like dust grains to be assembled or extends the survival
period in the quasar nuclear region. The process, and physical and chemical
conditions deserve further observational study and investigation.


title:
Electronic Structure of ThPd$_2$Al$_3$: an impact of the U $5f$ states
  in the electronic structure of UPd$_2$Al$_3$

abstract:
  The electronic structure of ThPd$_2$Al$_3$, which is isostructural to the
heavy fermion superconductor UPd$_2$Al$_3$, was investigated by photoelectron
spectroscopy. The band structure and Fermi surfaces of ThPd$_2$Al$_3$ were
obtained by angle-resolved photoelectron spectroscopy (ARPES), and the results
were well-explained by the band-structure calculation based on the local
density approximation. The comparison between the ARPES spectra and the
band-structure calculation suggests that the Fermi surface of ThPd$_2$Al$_3$
mainly consists of the Al $3p$ and Th $6d$ states with a minor contribution
from the Pd $4d$ states. The comparison of the band structures between
ThPd$_2$Al$_3$ and UPd$_2$Al$_3$ argues that the U $5f$ states form Fermi
surfaces in UPd$_2$Al$_3$ through hybridization with the Al $3p$ state in the
Al layer, suggesting that the Fermi surface of UPd$_2$Al$_3$ has a strong
three-dimensional nature.


title:
Data adaptive RKHS Tikhonov regularization for learning kernels in
  operators

abstract:
  We present DARTR: a Data Adaptive RKHS Tikhonov Regularization method for the
linear inverse problem of nonparametric learning of function parameters in
operators. A key ingredient is a system intrinsic data-adaptive (SIDA) RKHS,
whose norm restricts the learning to take place in the function space of
identifiability. DARTR utilizes this norm and selects the regularization
parameter by the L-curve method. We illustrate its performance in examples
including integral operators, nonlinear operators and nonlocal operators with
discrete synthetic data. Numerical results show that DARTR leads to an accurate
estimator robust to both numerical error due to discrete data and noise in
data, and the estimator converges at a consistent rate as the data mesh refines
under different levels of noises, outperforming two baseline regularizers using
$l^2$ and $L^2$ norms.


title:
Aggregate Queries on Knowledge Graphs: Fast Approximation with
  Semantic-aware Sampling

abstract:
  A knowledge graph (KG) manages large-scale and real-world facts as a big
graph in a schema-flexible manner. Aggregate query is a fundamental query over
KGs, e.g., "what is the average price of cars produced in Germany?". Despite
its importance, answering aggregate queries on KGs has received little
attention in the literature. Aggregate queries can be supported based on
factoid queries, e.g., "find all cars produced in Germany", by applying an
additional aggregate operation on factoid queries' answers. However, this
straightforward method is challenging because both the accuracy and efficiency
of factoid query processing will seriously impact the performance of aggregate
queries. In this paper, we propose a "sampling-estimation" model to answer
aggregate queries over KGs, which is the first work to provide an approximate
aggregate result with an effective accuracy guarantee, and without relying on
factoid queries. Specifically, we first present a semantic-aware sampling to
collect a high-quality random sample through a random walk based on knowledge
graph embedding. Then, we propose unbiased estimators for COUNT, SUM, and a
consistent estimator for AVG to compute the approximate aggregate results based
on the random sample, with an accuracy guarantee in the form of confidence
interval. We extend our approach to support iterative improvement of accuracy,
and more complex queries with filter, GROUP-BY, and different graph shapes,
e.g., chain, cycle, star, flower. Extensive experiments over real-world KGs
demonstrate the effectiveness and efficiency of our approach.


title:
Charge Density Wave order and electron-phonon coupling in ternary
  superconductor Bi$_2$Rh$_3$Se$_2$

abstract:
  The newly discovered ternary chalcogenide superconductor Bi$_2$Rh$_3$Se$_2$
has attracted growing attention, which provides an opportunity to explore the
interplay between charge density wave (CDW) order and superconductivity.
However, whether the phase transition at 240 K can be attributed to CDW
formation remains controversial. To help resolve the debate, we study the
electronic structure study of Bi2Rh3Se2 by angle-resolved photoemission
spectroscopy experiments, with emphasis on the nature of its high-temperature
phase transition at 240 K. Our measurements demonstrate that the phase
transition at 240 K is a second-order CDW phase transition. Our results reveal
(i) a 2 x 2 CDW order in Bi$_2$Rh$_3$Se$_2$, accompanied by the reconstruction
of electronic structure, such as band folding, band splitting, and opening of
CDW gaps at and away from Fermi level; (ii) the existence of electron-phonon
coupling, which is manifested as an obvious kink and peak-dip-hump structure in
dispersion; and (iii) the appearance of a flat band. Our observations thus
enable us to shed light on the nature of the CDW order and its interplay with
superconductivity in Bi$_2$Rh$_3$Se$_2$.


title:
YONO: Modeling Multiple Heterogeneous Neural Networks on
  Microcontrollers

abstract:
  With the advancement of Deep Neural Networks (DNN) and large amounts of
sensor data from Internet of Things (IoT) systems, the research community has
worked to reduce the computational and resource demands of DNN to compute on
low-resourced microcontrollers (MCUs). However, most of the current work in
embedded deep learning focuses on solving a single task efficiently, while the
multi-tasking nature and applications of IoT devices demand systems that can
handle a diverse range of tasks (activity, voice, and context recognition) with
input from a variety of sensors, simultaneously.
  In this paper, we propose YONO, a product quantization (PQ) based approach
that compresses multiple heterogeneous models and enables in-memory model
execution and switching for dissimilar multi-task learning on MCUs. We first
adopt PQ to learn codebooks that store weights of different models. Also, we
propose a novel network optimization and heuristics to maximize the compression
rate and minimize the accuracy loss. Then, we develop an online component of
YONO for efficient model execution and switching between multiple tasks on an
MCU at run time without relying on an external storage device.
  YONO shows remarkable performance as it can compress multiple heterogeneous
models with negligible or no loss of accuracy up to 12.37$\times$. Besides,
YONO's online component enables an efficient execution (latency of 16-159 ms
per operation) and reduces model loading/switching latency and energy
consumption by 93.3-94.5% and 93.9-95.0%, respectively, compared to external
storage access. Interestingly, YONO can compress various architectures trained
with datasets that were not shown during YONO's offline codebook learning phase
showing the generalizability of our method. To summarize, YONO shows great
potential and opens further doors to enable multi-task learning systems on
extremely resource-constrained devices.


title:
Semantic-Preserving Linguistic Steganography by Pivot Translation and
  Semantic-Aware Bins Coding

abstract:
  Linguistic steganography (LS) aims to embed secret information into a highly
encoded text for covert communication. It can be roughly divided to two main
categories, i.e., modification based LS (MLS) and generation based LS (GLS).
Unlike MLS that hides secret data by slightly modifying a given text without
impairing the meaning of the text, GLS uses a trained language model to
directly generate a text carrying secret data. A common disadvantage for MLS
methods is that the embedding payload is very low, whose return is well
preserving the semantic quality of the text. In contrast, GLS allows the data
hider to embed a high payload, which has to pay the high price of
uncontrollable semantics. In this paper, we propose a novel LS method to modify
a given text by pivoting it between two different languages and embed secret
data by applying a GLS-like information encoding strategy. Our purpose is to
alter the expression of the given text, enabling a high payload to be embedded
while keeping the semantic information unchanged. Experimental results have
shown that the proposed work not only achieves a high embedding payload, but
also shows superior performance in maintaining the semantic consistency and
resisting linguistic steganalysis.


title:
PAMI-AD: An Activity Detector Exploiting Part-attention and Motion
  Information in Surveillance Videos

abstract:
  Activity detection in surveillance videos is a challenging task caused by
small objects, complex activity categories, its untrimmed nature, etc. In this
work, we propose an effective activity detection system for person-only and
vehicle-only activities in untrimmed surveillance videos, named PAMI-AD. It
consists of four modules, i.e., multi-object tracking, background modeling,
activity classifier and post-processing. In particular, we propose a novel
part-attention mechanism for person-only activities and a simple but strong
motion information encoding method for vehicle-only activities. Our proposed
system achieves the best results on the VIRAT dataset. Furthermore, our team
won the 1st place in the TRECVID 2021 ActEV challenge.


title:
Learning Sensorimotor Primitives of Sequential Manipulation Tasks from
  Visual Demonstrations

abstract:
  This work aims to learn how to perform complex robot manipulation tasks that
are composed of several, consecutively executed low-level sub-tasks, given as
input a few visual demonstrations of the tasks performed by a person. The
sub-tasks consist of moving the robot's end-effector until it reaches a
sub-goal region in the task space, performing an action, and triggering the
next sub-task when a pre-condition is met. Most prior work in this domain has
been concerned with learning only low-level tasks, such as hitting a ball or
reaching an object and grasping it. This paper describes a new neural
network-based framework for learning simultaneously low-level policies as well
as high-level policies, such as deciding which object to pick next or where to
place it relative to other objects in the scene. A key feature of the proposed
approach is that the policies are learned directly from raw videos of task
demonstrations, without any manual annotation or post-processing of the data.
Empirical results on object manipulation tasks with a robotic arm show that the
proposed network can efficiently learn from real visual demonstrations to
perform the tasks, and outperforms popular imitation learning algorithms.


title:
New Insights on Reducing Abrupt Representation Change in Online
  Continual Learning

abstract:
  In the online continual learning paradigm, agents must learn from a changing
distribution while respecting memory and compute constraints. Experience Replay
(ER), where a small subset of past data is stored and replayed alongside new
data, has emerged as a simple and effective learning strategy. In this work, we
focus on the change in representations of observed data that arises when
previously unobserved classes appear in the incoming data stream, and new
classes must be distinguished from previous ones. We shed new light on this
question by showing that applying ER causes the newly added classes'
representations to overlap significantly with the previous classes, leading to
highly disruptive parameter updates. Based on this empirical analysis, we
propose a new method which mitigates this issue by shielding the learned
representations from drastic adaptation to accommodate new classes. We show
that using an asymmetric update rule pushes new classes to adapt to the older
ones (rather than the reverse), which is more effective especially at task
boundaries, where much of the forgetting typically occurs. Empirical results
show significant gains over strong baselines on standard continual learning
benchmarks


title:
Sub-Terahertz Channel Measurements and Characterization in a Factory
  Building

abstract:
  Sub-Terahertz (THz) frequencies between 100 GHz and 300 GHz are being
considered as a key enabler for the sixth-generation (6G) wireless
communications due to the vast amounts of unused spectrum. The 3rd Generation
Partnership Project (3GPP) included the indoor industrial environments as a
scenario of interest since Release 15. This paper presents recent sub-THz
channel measurements using directional horn antennas of 27 dBi gain at 142 GHz
in a factory building, which hosts equipment manufacturing startups.
Directional measurements with co-polarized and cross-polarized antenna
configurations were conducted over distances from 6 to 40 meters.
Omnidirectional and directional path loss with two antenna polarization
configurations produce the gross cross-polarization discrimination (XPD) with a
mean of 27.7 dB, which suggests that dual-polarized antenna arrays can provide
good multiplexing gain for sub-THz wireless systems. The measured power delay
profile and power angular spectrum show the maximum root mean square (RMS)
delay spread of 66.0 nanoseconds and the maximum RMS angular spread of 103.7
degrees using a 30 dB threshold, indicating the factory scenario is a
rich-scattering environment due to a massive number of metal structures and
objects. This work will facilitate emerging sub-THz applications such as
super-resolution sensing and positioning for future smart factories.


title:
Unknown-Aware Object Detection: Learning What You Don't Know from Videos
  in the Wild

abstract:
  Building reliable object detectors that can detect out-of-distribution (OOD)
objects is critical yet underexplored. One of the key challenges is that models
lack supervision signals from unknown data, producing overconfident predictions
on OOD objects. We propose a new unknown-aware object detection framework
through Spatial-Temporal Unknown Distillation (STUD), which distills unknown
objects from videos in the wild and meaningfully regularizes the model's
decision boundary. STUD first identifies the unknown candidate object proposals
in the spatial dimension, and then aggregates the candidates across multiple
video frames to form a diverse set of unknown objects near the decision
boundary. Alongside, we employ an energy-based uncertainty regularization loss,
which contrastively shapes the uncertainty space between the in-distribution
and distilled unknown objects. STUD establishes the state-of-the-art
performance on OOD detection tasks for object detection, reducing the FPR95
score by over 10% compared to the previous best method. Code is available at
https://github.com/deeplearning-wisc/stud.


title:
Effect of viscosity on surface acoustic wave driven collective particle
  dynamics in sessile droplets: nebula, black holes and white dwarfs

abstract:
  Surface acoustic waves (SAW) can concentrate micro-particles in droplets
within seconds. Yet, the mechanism is not clear and existing explanations fail
by several orders of magnitude. In this paper, we analyze the effect of fluid
viscosity and particle size on SAW-driven collective particle dynamics in
droplets. In most of our experiments, the particles do not aggregate but
instead remain away from the droplet center, thereby forming "black holes". We
show that the black holes are due to steric hindrance wherein the poloidal
streamlines that should drive particles to the center of the droplet come too
close to the solid, so that the particles carried along these streamlines touch
the solid wall on the edge of the black hole before reaching the center of the
droplet. The size of these black holes is correlated with the size of the
aggregates formed in less viscous droplets. This suggests a common formation
mechanism for black holes and white dwarfs (aggregates). In the former, the
particles touching the solid would be washed away by the fluid, whereas in the
latter the particles would remain in contact with the solid and roll to the
center of the droplet where an aggregate is formed. We also discuss the
stability conditions of the aggregate at the bottom of the droplet. The concept
of hydrodynamic shielding is then used to concentrate 1 $\mu$m particles using
10 $\mu$m beads as shields.


title: Understanding Iterative Revision from Human-Written Text

abstract:
  Writing is, by nature, a strategic, adaptive, and more importantly, an
iterative process. A crucial part of writing is editing and revising the text.
Previous works on text revision have focused on defining edit intention
taxonomies within a single domain or developing computational models with a
single level of edit granularity, such as sentence-level edits, which differ
from human's revision cycles. This work describes IteraTeR: the first
large-scale, multi-domain, edit-intention annotated corpus of iteratively
revised text. In particular, IteraTeR is collected based on a new framework to
comprehensively model the iterative text revisions that generalize to various
domains of formal writing, edit intentions, revision depths, and granularities.
When we incorporate our annotated edit intentions, both generative and
edit-based text revision models significantly improve automatic evaluations.
Through our work, we better understand the text revision process, making vital
connections between edit intentions and writing quality, enabling the creation
of diverse corpora to support computational modeling of iterative text
revisions.


title:
Secure two-way fiber-optic time transfer against sub-ns asymmetric delay
  attack

abstract:
  Two-way fiber-optic time transfer is a promising precise time synchronization
technique with sub-nanosecond accuracy. However, asymmetric delay attack is a
serious threat which cannot be prevent by any encryption method. In this paper,
a dynamic model based scheme is proposed to defense the sub-nanosecond
asymmetric delay attack. A threshold is set according to the estimated time
difference by a two-state clock model where the fixed frequency difference is
excluded from the time difference to detect the asymmetric delay attack which
is smaller than the time difference induced by the fixed frequency difference.
Theoretical simulation and experimental demonstration are implemented to prove
the feasibility of the scheme. A two-way fiber-optic time transfer system with
time stability with 24.5ps, 3.98ps, and 2.95ps at 1s, 10s, and 100s averaging
time is shown under sub-ns asymmetric time delay attack experimentally. The
proposed method provides a promising secure sub-ns precise time synchronization
technique against asymmetric delay attack.


title:
Low-temperature specific-heat studies on two square-kagome
  antiferromagnets

abstract:
  We studied the low-temperature specific heats of two antiferromagnets with
the two-dimensional square-kagome structure, i.e.,
KCu$_6$AlBiO$_4$(SO$_4$)$_5$Cl (KCu6) and
Na$_6$Cu$_7$BiO$_4$(PO$_4$)$_4$[Cl,(OH)]$_3$ (NaCu7) with the structural
differences that there are interlayer Cu$^{2+}$ ions in NaCu7. Both materials
show no magnetic ordering down to 50 mK. At zero field, the $C/T$ of KCu6 has a
finite value when the temperature is close to zero K. Under the magnetic field,
a seemingly $T^2$ dependence appears and its coefficient is progressively
suppressed by the field. For NaCu7, the specific heat exhibits the $T^2$
dependence at zero field and under fields. The ratio of the quadratic
coefficients of KCu6 and NaCu7 at high fields is inversely proportional to
ratio of the squared Weiss temperatures, which indicates these two compounds
host the same ground state under fields. Our results suggest that the
interlayer Cu$^{2+}$ ions in NaCu7 play a negligible role in determination of
its ground state. We discuss the possible quantum-spin-liquid states in these
compounds and further directions to pursue based our results.


title:
Discrete Robust Control of Robot Manipulators using an Uncertainty and
  Disturbance Estimator

abstract:
  This article presents the design of a robust observer based on the
discrete-time formulation of Uncertainty and Disturbance Estimator (UDE), a
well-known robust control technique, for the purpose of controlling robot
manipulators. The design results in a complete closed-loop, robust,
controller--observer structure. The observer incorporates the estimate of the
overall uncertainty associated with the plant, in order to mimic its dynamics,
and the control law is generated using an auxiliary error instead of state
tracking error. A detailed qualitative and quantitative stability analysis is
provided, and simulations are performed on the two-link robot manipulator
system. Further, a comparative study with well-known control strategies for
robot manipulators is presented. The results demonstrate the efficacy of the
proposed technique, with better tracking performance and lower control energy
compared to other strategies.


title: Panoramic Human Activity Recognition

abstract:
  To obtain a more comprehensive activity understanding for a crowded scene, in
this paper, we propose a new problem of panoramic human activity recognition
(PAR), which aims to simultaneous achieve the individual action, social group
activity, and global activity recognition. This is a challenging yet practical
problem in real-world applications. For this problem, we develop a novel
hierarchical graph neural network to progressively represent and model the
multi-granularity human activities and mutual social relations for a crowd of
people. We further build a benchmark to evaluate the proposed method and other
existing related methods. Experimental results verify the rationality of the
proposed PAR problem, the effectiveness of our method and the usefulness of the
benchmark. We will release the source code and benchmark to the public for
promoting the study on this problem.


title:
Photonic unsupervised learning processor for secure and high-throughput
  optical fiber communication

abstract:
  Following the explosive growth of global data, there is an ever-increasing
demand for high-throughput optical fiber communication (OFC) systems to perform
massive data transmission and processing. Existing OFC methods mainly rely on
electronic circuits for data processing, which severely limits the
communication throughput. Though considered promising for the next-generation
high-speed fiber communication, all-optical OFC remains unachievable due to
serious challenges in effective optical computing, system modeling and
configuring. Here we propose an end-to-end photonic encoder-decoder (PED)
processor which maps the physical system of OFC into an optical generative
neural network. By modeling the OFC transmission process as the variation in
the constructed optical latent space, the PED learns noise-resistant coding
schemes via unsupervised optimization. With multi-layer parametric diffractive
neural networks, the PED establishes a large-scale and high-throughput optical
computing framework that integrates the main OFC computations including coding,
encryption and compression to the optical domain. The whole system improves the
latency of computation in OFC systems by five orders of magnitude compared with
the state-of-the-art device. On benchmarking datasets, the PED experimentally
achieves up to 32% reduction in transmission error ratio (ER) than on-off
keying (OOK), one of the mainstream methods with the lowest ER in general
transmission. As we demonstrate on medical data, the PED increases the
transmission throughput by two orders of magnitude than 8-level pulse amplitude
modulation (PAM-8). We believe the proposed photonic encoder-decoder processor
not only paves the way to the next-generation all-optical OFC systems, but also
promotes a wide range of AI-based physical system designs.


title:
A Fast Scale-Invariant Algorithm for Non-negative Least Squares with
  Non-negative Data

abstract:
  Nonnegative (linear) least square problems are a fundamental class of
problems that is well-studied in statistical learning and for which solvers
have been implemented in many of the standard programming languages used within
the machine learning community. The existing off-the-shelf solvers view the
non-negativity constraint in these problems as an obstacle and, compared to
unconstrained least squares, perform additional effort to address it. However,
in many of the typical applications, the data itself is nonnegative as well,
and we show that the nonnegativity in this case makes the problem easier. In
particular, while the oracle complexity of unconstrained least squares problems
necessarily scales with one of the data matrix constants (typically the
spectral norm) and these problems are solved to additive error, we show that
nonnegative least squares problems with nonnegative data are solvable to
multiplicative error and with complexity that is independent of any matrix
constants. The algorithm we introduce is accelerated and based on a primal-dual
perspective. We further show how to provably obtain linear convergence using
adaptive restart coupled with our method and demonstrate its effectiveness on
large-scale data via numerical experiments.


title:
Image Search with Text Feedback by Additive Attention Compositional
  Learning

abstract:
  Effective image retrieval with text feedback stands to impact a range of
real-world applications, such as e-commerce. Given a source image and text
feedback that describes the desired modifications to that image, the goal is to
retrieve the target images that resemble the source yet satisfy the given
modifications by composing a multi-modal (image-text) query. We propose a novel
solution to this problem, Additive Attention Compositional Learning (AACL),
that uses a multi-modal transformer-based architecture and effectively models
the image-text contexts. Specifically, we propose a novel image-text
composition module based on additive attention that can be seamlessly plugged
into deep neural networks. We also introduce a new challenging benchmark
derived from the Shopping100k dataset. AACL is evaluated on three large-scale
datasets (FashionIQ, Fashion200k, and Shopping100k), each with strong
baselines. Extensive experiments show that AACL achieves new state-of-the-art
results on all three datasets.


title:
Towards Efficient Data-Centric Robust Machine Learning with Noise-based
  Augmentation

abstract:
  The data-centric machine learning aims to find effective ways to build
appropriate datasets which can improve the performance of AI models. In this
paper, we mainly focus on designing an efficient data-centric scheme to improve
robustness for models towards unforeseen malicious inputs in the black-box test
settings. Specifically, we introduce a noised-based data augmentation method
which is composed of Gaussian Noise, Salt-and-Pepper noise, and the PGD
adversarial perturbations. The proposed method is built on lightweight
algorithms and proved highly effective based on comprehensive evaluations,
showing good efficiency on computation cost and robustness enhancement. In
addition, we share our insights about the data-centric robust machine learning
gained from our experiments.


title: Considerations for a TeV Collider Based On Dielectric Laser Accelerators

abstract:
  Particle acceleration in dielectric microstructures powered by infrared
lasers, or "dielectric laser acceleration" (DLA), is a promising area of
advanced accelerator research with the potential to enable more affordable and
higher-gradient accelerators for energy frontier science and a variety of other
applications. DLA leverages well-established industrial fabrication
capabilities and the commercial availability of tabletop lasers to reduce cost,
with axial accelerating fields in the GV/m range. Desirable luminosities would
be obtained by operating with very low charge per bunch but at extremely high
repetition rates. And as a consequence of its unique operating parameter
regime, coupling of the laser to the accelerator can potentially be in the 50\%
range and with low beamstrahlung energy loss due at the interaction point,
making DLA a promising approach for a future multi-TeV linear collider.


title:
SpeechFormer: A Hierarchical Efficient Framework Incorporating the
  Characteristics of Speech

abstract:
  Transformer has obtained promising results on cognitive speech signal
processing field, which is of interest in various applications ranging from
emotion to neurocognitive disorder analysis. However, most works treat speech
signal as a whole, leading to the neglect of the pronunciation structure that
is unique to speech and reflects the cognitive process. Meanwhile, Transformer
has heavy computational burden due to its full attention operation. In this
paper, a hierarchical efficient framework, called SpeechFormer, which considers
the structural characteristics of speech, is proposed and can be served as a
general-purpose backbone for cognitive speech signal processing. The proposed
SpeechFormer consists of frame, phoneme, word and utterance stages in
succession, each performing a neighboring attention according to the structural
pattern of speech with high computational efficiency. SpeechFormer is evaluated
on speech emotion recognition (IEMOCAP & MELD) and neurocognitive disorder
detection (Pitt & DAIC-WOZ) tasks, and the results show that SpeechFormer
outperforms the standard Transformer-based framework while greatly reducing the
computational cost. Furthermore, our SpeechFormer achieves comparable results
to the state-of-the-art approaches.


title: Dense Urban Outdoor-Indoor Coverage from 3.5 to 28 GHz

abstract:
  In the US, people spend 87% of their time indoors and have an average of four
connected devices per person (in 2020). As such, providing indoor coverage has
always been a challenge but becomes even more difficult as carrier frequencies
increase to mmWave and beyond. This paper investigates the outdoor and
outdoor-indoor coverage of an urban network comparing globally standardized
building penetration models and implementing models to corresponding scenarios.
The glass used in windows of buildings in the grid plays a pivotal role in
determining the outdoor-to-indoor propagation loss. For 28 GHz with 1
W/polarization transmit power in the urban street grid, the downlink data rates
for 90% of outdoor users are estimated at over 250 Mbps. In contrast, 15% of
indoor users are estimated to be in outage, with SNR $<-$3 dB when base
stations are 400 m apart with one-fifth of the buildings imposing high
penetration loss ($\sim$ 35 dB). At 3.5 GHz, base stations may achieve over 250
Mbps for 90% indoor users if 400 MHz bandwidth with 100 W/polarization transmit
power is available. The methods and models presented can be used to facilitate
decisions regarding the density and transmit power required to provide high
data rates to majority users in urban centers.


title:
Generating 3D Bio-Printable Patches Using Wound Segmentation and
  Reconstruction to Treat Diabetic Foot Ulcers

abstract:
  We introduce AiD Regen, a novel system that generates 3D wound models
combining 2D semantic segmentation with 3D reconstruction so that they can be
printed via 3D bio-printers during the surgery to treat diabetic foot ulcers
(DFUs). AiD Regen seamlessly binds the full pipeline, which includes RGB-D
image capturing, semantic segmentation, boundary-guided point-cloud processing,
3D model reconstruction, and 3D printable G-code generation, into a single
system that can be used out of the box. We developed a multi-stage data
preprocessing method to handle small and unbalanced DFU image datasets. AiD
Regen's human-in-the-loop machine learning interface enables clinicians to not
only create 3D regenerative patches with just a few touch interactions but also
customize and confirm wound boundaries. As evidenced by our experiments, our
model outperforms prior wound segmentation models and our reconstruction
algorithm is capable of generating 3D wound models with compelling accuracy. We
further conducted a case study on a real DFU patient and demonstrated the
effectiveness of AiD Regen in treating DFU wounds.


title:
Online Target Localization using Adaptive Belief Propagation in the HMM
  Framework

abstract:
  This paper proposes a novel adaptive sample space-based Viterbi algorithm for
ultra-wideband (UWB) based target localization in an online manner. As the
discretized area of interest is defined as a finite number of hidden states,
the most probable trajectory of the unspecified agent is computed efficiently
via dynamic programming in a Hidden Markov Model (HMM) framework. Furthermore,
the approach has no requirements about Gaussian assumption and linearization
for Bayesian calculation. However, the issue of computational complexity
becomes very critical as the number of hidden states increases for estimation
accuracy and large space. Previous localization works, based on discrete-state
HMM, handle a small number of hidden variables, which represent specific paths
or places. Inspired by the k-d Tree algorithm (e.g., quadtree) that is commonly
used in the computer vision field, we propose a belief propagation in the most
probable belief space with a low to high-resolution sequentially, thus reducing
the required resources significantly. Our method has three advantages for
localization: (a) no Gaussian assumptions and linearization, (b) handling the
whole area of interest, not specific or small map representations, (c) reducing
computation time and required memory size. Experimental tests demonstrate our
results.


title: Quantum Volume in Practice: What Users Can Expect from NISQ Devices

abstract:
  Quantum volume (QV) has become the de-facto standard benchmark to quantify
the capability of Noisy Intermediate-Scale Quantum (NISQ) devices. While QV
values are often reported by NISQ providers for their systems, we perform our
own series of QV calculations on more than 20 NISQ devices currently
(2021/2022) offered by IBM Q, IonQ, Rigetti, Oxford Quantum Circuits, and
Quantinuum (formerly Honeywell). Our approach characterizes the performances
that an advanced user of these NISQ devices can expect to achieve with a
reasonable amount of optimization, but without white-box access to the device.
In particular, we compile QV circuits to standard gate sets of the vendor using
compiler optimization routines where available, and we perform experiments
across different initial qubit mappings. We find that running QV tests requires
very significant compilation cycles, QV values achieved in our tests typically
lag behind officially reported results and also depend significantly on the
classical compilation effort invested.


title:
Connecting Low- and High-Redshift Weak Emission-Line Quasars via HST
  Spectroscopy of Ly$\alpha$ Emission

abstract:
  We present ultraviolet spectroscopy covering the Ly$\alpha$ + N V complex of
six candidate low-redshift ($0.9 < z < 1.5$) weak emission-line quasars (WLQs)
based on observations with the Hubble Space Telescope. The original systematic
searches for these puzzling Type 1 quasars with intrinsically weak broad
emission lines revealed an $N \approx 100$ WLQ population from optical
spectroscopy of high-redshift ($z > 3$) quasars, defined by a Ly$\alpha$ + N V
rest-frame equivalent width (EW) threshold $< 15.4$ \r{A}. Identification of
lower-redshift ($z < 3$) WLQ candidates, however, has relied primarily on
optical spectroscopy of weak broad emission lines at longer rest-frame
wavelengths. With these new observations expanding existing optical coverage
into the ultraviolet, we explore unifying the low- and high-$z$ WLQ populations
via EW[Ly$\alpha$+NV]. Two objects in the sample unify with high-$z$ WLQs,
three others appear consistent with the intermediate portion of the population
connecting WLQs and normal quasars, and the final object is consistent with
typical quasars. The expanded wavelength coverage improves the number of
available line diagnostics for our individual targets, allowing a better
understanding of the shapes of their ionizing continua. The ratio of
EW[Ly$\alpha$+NV] to EW[MgII] in our sample is generally small but varied,
favoring a soft ionizing continuum scenario for WLQs, and we find a lack of
correlation between EW[Ly$\alpha$+NV] and the X-ray properties of our targets,
consistent with a "slim-disk" shielding gas model. We also find indications
that weak absorption may be a more significant contaminant in low-$z$ WLQ
populations than previously thought.


title:
Shadows can be Dangerous: Stealthy and Effective Physical-world
  Adversarial Attack by Natural Phenomenon

abstract:
  Estimating the risk level of adversarial examples is essential for safely
deploying machine learning models in the real world. One popular approach for
physical-world attacks is to adopt the "sticker-pasting" strategy, which
however suffers from some limitations, including difficulties in access to the
target or printing by valid colors. A new type of non-invasive attacks emerged
recently, which attempt to cast perturbation onto the target by optics based
tools, such as laser beam and projector. However, the added optical patterns
are artificial but not natural. Thus, they are still conspicuous and
attention-grabbed, and can be easily noticed by humans. In this paper, we study
a new type of optical adversarial examples, in which the perturbations are
generated by a very common natural phenomenon, shadow, to achieve naturalistic
and stealthy physical-world adversarial attack under the black-box setting. We
extensively evaluate the effectiveness of this new attack on both simulated and
real-world environments. Experimental results on traffic sign recognition
demonstrate that our algorithm can generate adversarial examples effectively,
reaching 98.23% and 90.47% success rates on LISA and GTSRB test sets
respectively, while continuously misleading a moving camera over 95% of the
time in real-world scenarios. We also offer discussions about the limitations
and the defense mechanism of this attack.


title: Table Structure Recognition with Conditional Attention

abstract:
  Tabular data in digital documents is widely used to express compact and
important information for readers. However, it is challenging to parse tables
from unstructured digital documents, such as PDFs and images, into
machine-readable format because of the complexity of table structures and the
missing of meta-information. Table Structure Recognition (TSR) problem aims to
recognize the structure of a table and transform the unstructured tables into a
structured and machine-readable format so that the tabular data can be further
analysed by the down-stream tasks, such as semantic modeling and information
retrieval. In this study, we hypothesize that a complicated table structure can
be represented by a graph whose vertices and edges represent the cells and
association between cells, respectively. Then we define the table structure
recognition problem as a cell association classification problem and propose a
conditional attention network (CATT-Net). The experimental results demonstrate
the superiority of our proposed method over the state-of-the-art methods on
various datasets. Besides, we investigate whether the alignment of a cell
bounding box or a text-focused approach has more impact on the model
performance. Due to the lack of public dataset annotations based on these two
approaches, we further annotate the ICDAR2013 dataset providing both types of
bounding boxes, which can be a new benchmark dataset for evaluating the methods
in this field. Experimental results show that the alignment of a cell bounding
box can help improve the Micro-averaged F1 score from 0.915 to 0.963, and the
Macro-average F1 score from 0.787 to 0.923.


title: A Variational Hierarchical Model for Neural Cross-Lingual Summarization

abstract:
  The goal of the cross-lingual summarization (CLS) is to convert a document in
one language (e.g., English) to a summary in another one (e.g., Chinese).
Essentially, the CLS task is the combination of machine translation (MT) and
monolingual summarization (MS), and thus there exists the hierarchical
relationship between MT\&MS and CLS. Existing studies on CLS mainly focus on
utilizing pipeline methods or jointly training an end-to-end model through an
auxiliary MT or MS objective. However, it is very challenging for the model to
directly conduct CLS as it requires both the abilities to translate and
summarize. To address this issue, we propose a hierarchical model for the CLS
task, based on the conditional variational auto-encoder. The hierarchical model
contains two kinds of latent variables at the local and global levels,
respectively. At the local level, there are two latent variables, one for
translation and the other for summarization. As for the global level, there is
another latent variable for cross-lingual summarization conditioned on the two
local-level variables. Experiments on two language directions (English-Chinese)
verify the effectiveness and superiority of the proposed approach. In addition,
we show that our model is able to generate better cross-lingual summaries than
comparison models in the few-shot setting.


title: Coarse-to-Fine Vision Transformer

abstract:
  Vision Transformers (ViT) have made many breakthroughs in computer vision
tasks. However, considerable redundancy arises in the spatial dimension of an
input image, leading to massive computational costs. Therefore, We propose a
coarse-to-fine vision transformer (CF-ViT) to relieve computational burden
while retaining performance in this paper. Our proposed CF-ViT is motivated by
two important observations in modern ViT models: (1) The coarse-grained patch
splitting can locate informative regions of an input image. (2) Most images can
be well recognized by a ViT model in a small-length token sequence. Therefore,
our CF-ViT implements network inference in a two-stage manner. At coarse
inference stage, an input image is split into a small-length patch sequence for
a computationally economical classification. If not well recognized, the
informative patches are identified and further re-split in a fine-grained
granularity. Extensive experiments demonstrate the efficacy of our CF-ViT. For
example, without any compromise on performance, CF-ViT reduces 53% FLOPs of
LV-ViT, and also achieves 2.01x throughput.


title: Virtual Displacement based Discontinuity Layout Optimization

abstract:
  Discontinuity layout optimization (DLO) is a relatively new upper bound limit
analysis method. Compared to classic topology optimization methods, aimed at
obtaining the optimum design of a structure by considering its self-weight,
building cost or bearing capacity, DLO optimizes the failure pattern of the
structure under specific loading conditions and constraints by minimizing the
dissipation energy. In this work, we present a modified DLO algorithm that
contains all of the advantages of DLO. It is referred to virtual
displacement-based discontinuity layout optimization (VDLO). VDLO takes the
stress state of a loaded structure as a snapshot and correspondingly provides
the optimum failure pattern, which greatly extends the application potential of
DLO. Numerical examples indicate the effectiveness and flexibility of VDLO. It
is regarded as a highly promising supplemental tool for other numerical methods
in element-/node-based frameworks.


title:
A Unified Framework of Medical Information Annotation and Extraction for
  Chinese Clinical Text

abstract:
  Medical information extraction consists of a group of natural language
processing (NLP) tasks, which collaboratively convert clinical text to
pre-defined structured formats. Current state-of-the-art (SOTA) NLP models are
highly integrated with deep learning techniques and thus require massive
annotated linguistic data. This study presents an engineering framework of
medical entity recognition, relation extraction and attribute extraction, which
are unified in annotation, modeling and evaluation. Specifically, the
annotation scheme is comprehensive, and compatible between tasks, especially
for the medical relations. The resulted annotated corpus includes 1,200 full
medical records (or 18,039 broken-down documents), and achieves inter-annotator
agreements (IAAs) of 94.53%, 73.73% and 91.98% F 1 scores for the three tasks.
Three task-specific neural network models are developed within a shared
structure, and enhanced by SOTA NLP techniques, i.e., pre-trained language
models. Experimental results show that the system can retrieve medical
entities, relations and attributes with F 1 scores of 93.47%, 67.14% and
90.89%, respectively. This study, in addition to our publicly released
annotation scheme and code, provides solid and practical engineering experience
of developing an integrated medical information extraction system.


title:
Uncertainty-aware molecular dynamics from Bayesian active learning:
  Phase Transformations and Thermal Transport in SiC

abstract:
  Machine learning interatomic force fields are promising for combining high
computational efficiency and accuracy in modeling quantum interactions and
simulating atomic level processes. Active learning methods have been recently
developed to train force fields efficiently and automatically. Among them,
Bayesian active learning utilizes principled uncertainty quantification to make
data acquisition decisions. In this work, we present an efficient Bayesian
active learning workflow, where the force field is constructed from a sparse
Gaussian process regression model based on atomic cluster expansion
descriptors. To circumvent the high computational cost of the sparse Gaussian
process uncertainty calculation, we formulate a high-performance approximate
mapping of the uncertainty and demonstrate a speedup of several orders of
magnitude. As an application, we train a model for silicon carbide (SiC), a
wide-gap semiconductor with complex polymorphic structure and diverse
technological applications in power electronics, nuclear physics and astronomy.
We show that the high pressure phase transformation is accurately captured by
the autonomous active learning workflow. The trained force field shows
excellent agreement with both ab initio calculations and experimental
measurements, and outperforms existing empirical models on vibrational and
thermal properties. The active learning workflow is readily generalized to a
wide range of systems, accelerates computational understanding and design.


title:
Incorporating Hierarchy into Text Encoder: a Contrastive Learning
  Approach for Hierarchical Text Classification

abstract:
  Hierarchical text classification is a challenging subtask of multi-label
classification due to its complex label hierarchy. Existing methods encode text
and label hierarchy separately and mix their representations for
classification, where the hierarchy remains unchanged for all input text.
Instead of modeling them separately, in this work, we propose Hierarchy-guided
Contrastive Learning (HGCLR) to directly embed the hierarchy into a text
encoder. During training, HGCLR constructs positive samples for input text
under the guidance of the label hierarchy. By pulling together the input text
and its positive sample, the text encoder can learn to generate the
hierarchy-aware text representation independently. Therefore, after training,
the HGCLR enhanced text encoder can dispense with the redundant hierarchy.
Extensive experiments on three benchmark datasets verify the effectiveness of
HGCLR.


title:
Field-tunable toroidal moment and anomalous Hall effect in noncollinear
  antiferromagnetic Weyl semimetal Co1/3TaS2

abstract:
  Combining magnetism with band topology provides various novel phases that are
otherwise impossible. Among several cases, noncollinear metallic
antiferromagnets can reveal particularly rich topological physics due to their
diverse magnetic ground states. However, there are only a few experimental
studies due to the lack of suitable materials, especially with triangular
lattice antiferromagnets. Here, we report that metallic triangular
antiferromagnet Co1/3TaS2 exhibits a substantial anomalous Hall effect (AHE)
related to its noncollinear magnetic order. Our first-principles calculations
found that hourglass Weyl fermions from the non-symmorphic symmetry trigger
AHE. We further show that AHE in Co1/3TaS2 can be characterized by the toroidal
moment, a vortex-like multipole component that arises from a combination of
chiral lattice and geometrical frustration. Finally, the unusual
field-tunability of the toroidal moment puts Co1/3TaS2 as a rare example of a
noncollinear metallic antiferromagnet filled with interesting magnetic and
topological properties.


title:
GANSpiration: Balancing Targeted and Serendipitous Inspiration in User
  Interface Design with Style-Based Generative Adversarial Network

abstract:
  Inspiration from design examples plays a crucial role in the creative process
of user interface design. However, current tools and techniques that support
inspiration usually only focus on example browsing with limited user control or
similarity-based example retrieval, leading to undesirable design outcomes such
as focus drift and design fixation. To address these issues, we propose the
GANSpiration approach that suggests design examples for both targeted and
serendipitous inspiration, leveraging a style-based Generative Adversarial
Network. A quantitative evaluation revealed that the outputs of
GANSpiration-based example suggestion approaches are relevant to the input
design, and at the same time include diverse instances. A user study with
professional UI/UX practitioners showed that the examples suggested by our
approach serve as viable sources of inspiration for overall design concepts and
specific design elements. Overall, our work paves the road of using advanced
generative machine learning techniques in supporting the creative design
practice.


title:
Informative Planning for Worst-Case Error Minimisation in Sparse
  Gaussian Process Regression

abstract:
  We present a planning framework for minimising the deterministic worst-case
error in sparse Gaussian process (GP) regression. We first derive a universal
worst-case error bound for sparse GP regression with bounded noise using
interpolation theory on reproducing kernel Hilbert spaces (RKHSs). By
exploiting the conditional independence (CI) assumption central to sparse GP
regression, we show that the worst-case error minimisation can be achieved by
solving a posterior entropy minimisation problem. In turn, the posterior
entropy minimisation problem is solved using a Gaussian belief space planning
algorithm. We corroborate the proposed worst-case error bound in a simple 1D
example, and test the planning framework in simulation for a 2D vehicle in a
complex flow field. Our results demonstrate that the proposed posterior entropy
minimisation approach is effective in minimising deterministic error, and
outperforms the conventional measurement entropy maximisation formulation when
the inducing points are fixed.


title: Graphene in complex magnetic fields

abstract:
  Exact analytic solutions for an electron in graphene interacting with
external complex magnetic fields are found. The eigenvalue problem for the
non-hermitian Dirac-Weyl Hamiltonian leads to a pair of intertwined
Schr{\"o}dinger equations, which are solved by means of supersymmetric quantum
mechanics. Making an analogy with the non-uniform strained graphene a
prospective physical interpretation for the complex magnetic field is given.
The probability and currents densities are explored and some remarkable
differences as compared with the real case are observed.


title:
Slice-Connection Clustering Algorithm for Tree Roots Recognition in
  Noisy 3D GPR Data

abstract:
  3D mapping of tree roots is a popular ground-penetrating radar (GPR)
application. In real field tests, the recognition of tree roots suffers due to
noisey reflection patterns from subsurface targets that are not of interest,
such as rocks, cavities, soil unevenness, etc. A Slice-Connection Clustering
Algorithm (SCC) is applied to separate the regions of interest from each other
in a reconstructed 3D image. The proposed method can successfully recognize the
radar signatures of the roots and distinguish roots from other objects.
Meanwhile, most noise radar features are ignored through our method. The final
3D mapping of the radargram obtained by the method can be used to estimate the
location and extension trend of the tree roots. The effectiveness of the
proposed system is tested on real GPR data.


title: Deep Rectangling for Image Stitching: A Learning Baseline

abstract:
  Stitched images provide a wide field-of-view (FoV) but suffer from unpleasant
irregular boundaries. To deal with this problem, existing image rectangling
methods devote to searching an initial mesh and optimizing a target mesh to
form the mesh deformation in two stages. Then rectangular images can be
generated by warping stitched images. However, these solutions only work for
images with rich linear structures, leading to noticeable distortions for
portraits and landscapes with non-linear objects. In this paper, we address
these issues by proposing the first deep learning solution to image
rectangling. Concretely, we predefine a rigid target mesh and only estimate an
initial mesh to form the mesh deformation, contributing to a compact one-stage
solution. The initial mesh is predicted using a fully convolutional network
with a residual progressive regression strategy. To obtain results with high
content fidelity, a comprehensive objective function is proposed to
simultaneously encourage the boundary rectangular, mesh shape-preserving, and
content perceptually natural. Besides, we build the first image stitching
rectangling dataset with a large diversity in irregular boundaries and scenes.
Experiments demonstrate our superiority over traditional methods both
quantitatively and qualitatively.


title:
The splitting algorithms by Ryu, by Malitsky-Tam, and by Campoy applied
  to normal cones of linear subspaces converge strongly to the projection onto
  the intersection

abstract:
  Finding a zero of a sum of maximally monotone operators is a fundamental
problem in modern optimization and nonsmooth analysis. Assuming that the
resolvents of the operators are available, this problem can be tackled with the
Douglas-Rachford algorithm. However, when dealing with three or more operators,
one must work in a product space with as many factors as there are operators.
In groundbreaking recent work by Ryu and by Malitsky and Tam, it was shown that
the number of factors can be reduced by one. A similar reduction was achieved
recently by Campoy through a clever reformulation originally proposed by
Kruger. All three splitting methods guarantee weak convergence to some solution
of the underlying sum problem; strong convergence holds in the presence of
uniform monotonicity.
  In this paper, we provide a case study when the operators involved are normal
cone operators of subspaces and the solution set is thus the intersection of
the subspaces. Even though these operators lack strict convexity, we show that
striking conclusions are available in this case: strong (instead of weak)
convergence and the solution obtained is (not arbitrary but) the projection
onto the intersection. Numerical experiments to illustrate our results are also
provided.


title:
Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point
  Clouds for Closing Domain Gap

abstract:
  Semantic analyses of object point clouds are largely driven by releasing of
benchmarking datasets, including synthetic ones whose instances are sampled
from object CAD models. However, learning from synthetic data may not
generalize to practical scenarios, where point clouds are typically incomplete,
non-uniformly distributed, and noisy. Such a challenge of Simulation-to-Real
(Sim2Real) domain gap could be mitigated via learning algorithms of domain
adaptation; however, we argue that generation of synthetic point clouds via
more physically realistic rendering is a powerful alternative, as systematic
non-uniform noise patterns can be captured. To this end, we propose an
integrated scheme consisting of physically realistic synthesis of object point
clouds via rendering stereo images via projection of speckle patterns onto CAD
models and a novel quasi-balanced self-training designed for more balanced data
distribution by sparsity-driven selection of pseudo labeled samples for long
tailed classes. Experiment results can verify the effectiveness of our method
as well as both of its modules for unsupervised domain adaptation on point
cloud classification, achieving the state-of-the-art performance.


title: Timelike minimal surfaces in the three-dimensional Heisenberg group

abstract:
  Timelike surfaces in the three-dimensional Heisenberg group with left
invariant semi-Riemannian metric are studied. In particular, non-vertical
timelike minimal surfaces are characterized by the non-conformal Lorentz
harmonic maps into the de Sitter two phere. On the basis of the
characterization, the generalized Weierstrass type representation will be
established through the loop group decompositions.


title:
Towards Building an Open-Domain Dialogue System Incorporated with
  Internet Memes

abstract:
  In recent years, Internet memes have been widely used in online chatting.
Compared with text-based communication, conversations become more expressive
and attractive when Internet memes are incorporated. This paper presents our
solutions for the Meme incorporated Open-domain Dialogue (MOD) Challenge of
DSTC10, where three tasks are involved: text response modeling, meme retrieval,
and meme emotion classification. Firstly, we leverage a large-scale pre-trained
dialogue model for coherent and informative response generation. Secondly,
based on interaction-based text-matching, our approach can retrieve appropriate
memes with good generalization ability. Thirdly, we propose to model the
emotion flow (EF) in conversations and introduce an auxiliary task of emotion
description prediction (EDP) to boost the performance of meme emotion
classification. Experimental results on the MOD dataset demonstrate that our
methods can incorporate Internet memes into dialogue systems effectively.


title:
An Efficient Two-Stage SPARC Decoder for Massive MIMO Unsourced Random
  Access

abstract:
  In this paper, we study a concatenate coding scheme based on sparse
regression code (SPARC) and tree code for unsourced random access in massive
multiple-input and multiple-output (MIMO) systems. Our focus is concentrated on
efficient decoding for the inner SPARC with practical concerns. A two-stage
method is proposed to achieve near-optimal performance while maintaining low
computational complexity. Specifically, an one-step thresholding-based
algorithm is first used for reducing large dimensions of the SPARC decoding,
after which an relaxed maximum-likelihood estimator is employed for refinement.
Adequate simulation results are provided to validate the near-optimal
performance and the low computational complexity. Besides, for covariance-based
sparse recovery method, theoretical analyses are given to characterize the
upper bound of the number of active users supported when convex relaxation is
considered, and the probability of successful dimension reduction by the
one-step thresholding-based algorithm.


title:
Focussing frustration for self-limiting assembly of flexible, curved
  particles

abstract:
  We show that geometric frustration in a broad class of deformable and
naturally curved, shell-like colloidal particles gives rise to self-limiting
assembly of finite-sized stacks that far exceed particle dimensions. When
inter-particle adhesions favor conformal stacking, particle shape requires {\it
curvature focussing} in the stack, leading to a super-extensive accumulation of
bending costs that ultimately limit the ground-state stack size to a finite
value. Using a combination of continuum theory and particle-based simulation,
we demonstrate that the self-limiting stack size is controlled by the ratio of
the intra-particle bending costs to inter-particle adhesion energy, ultimately
achieving assembly sizes that are tuned from a few, up to several tens of,
particles. We show that the range of self-limiting assembly is delimited by the
two structural modes of "frustration escape" which evade the thermodynamic
costs of curvature focussing. Crucially, each of these modes can be suppressed
through suitable choice of adhesive range and lateral patchiness of adhesion,
providing feasible strategies to program finite assembly size via the interplay
between shape-frustration, binding and deformability of colloidal building
blocks.


title:
Multi-Scale Self-Contrastive Learning with Hard Negative Mining for
  Weakly-Supervised Query-based Video Grounding

abstract:
  Query-based video grounding is an important yet challenging task in video
understanding, which aims to localize the target segment in an untrimmed video
according to a sentence query. Most previous works achieve significant progress
by addressing this task in a fully-supervised manner with segment-level labels,
which require high labeling cost. Although some recent efforts develop
weakly-supervised methods that only need the video-level knowledge, they
generally match multiple pre-defined segment proposals with query and select
the best one, which lacks fine-grained frame-level details for distinguishing
frames with high repeatability and similarity within the entire video. To
alleviate the above limitations, we propose a self-contrastive learning
framework to address the query-based video grounding task under a
weakly-supervised setting. Firstly, instead of utilizing redundant segment
proposals, we propose a new grounding scheme that learns frame-wise matching
scores referring to the query semantic to predict the possible foreground
frames by only using the video-level annotations. Secondly, since some
predicted frames (i.e., boundary frames) are relatively coarse and exhibit
similar appearance to their adjacent frames, we propose a coarse-to-fine
contrastive learning paradigm to learn more discriminative frame-wise
representations for distinguishing the false positive frames. In particular, we
iteratively explore multi-scale hard negative samples that are close to
positive samples in the representation space for distinguishing fine-grained
frame-wise details, thus enforcing more accurate segment grounding. Extensive
experiments on two challenging benchmarks demonstrate the superiority of our
proposed method compared with the state-of-the-art methods.


title: Hermite spectral method for multi-species Boltzmann equation

abstract:
  We introduce a numerical scheme for the full multi-species Boltzmann equation
based on Hermite spectral method. With the proper choice of expansion centers
for different species, a practical algorithm is derived to evaluate the
complicated multi-species binary collision operator. New collision models are
built by combining the quadratic collision model and the simple BGK collision
model under the framework of the Hermite spectral method, which enables us to
balance the computational cost and accuracy. Several numerical experiments are
implemented to validate the dramatic efficiency of this new Hermite spectral
method. Moreover, we can handle the problems with as many as 100 species, which
is far beyond the capability of the state-of-art algorithms.


title: Newton-Okounkov bodies of chemical reaction systems

abstract:
  Despite their noted potential in polynomial-system solving, there are few
concrete examples of Newton-Okounkov bodies arising from applications.
Accordingly, in this paper, we introduce a new application of Newton-Okounkov
body theory to the study of chemical reaction networks, and compute several
examples. An important invariant of a chemical reaction network is its maximum
number of positive steady states, which is realized as the maximum number of
positive real roots of a parametrized polynomial system. Here, we introduce a
new upper bound on this number, namely the `Newton-Okounkov body bound' of a
chemical reaction network. Through explicit examples, we show that the
Newton-Okounkov body bound of a network gives a good upper bound on its maximum
number of positive steady states. We also compare this Newton-Okounkov body
bound to a related upper bound, namely the mixed volume of a chemical reaction
network, and find that it often achieves better bounds.


title:
Coupling between improper ferroelectricity and ferrimagnetism in
  hexagonal ferrites

abstract:
  Antisymmetric Dzyaloshinskii-Moriya (DM) interactions generating from the
spin-orbit coupling induce various fascinating properties, like magnetoelectric
(ME) effect, weak ferromagnetism and non-trivial topological spin textures like
skyrmions, in real materials. Compared to their symmetric isotropic exchange
counterpart, these interactions are generally of a weaker order of strength,
creating modest twisting in the spin structure which results in weak
ferromagntism or weak linear ME effect. Our proposed two-sublattice model, in
contrast, predicts a hitherto unobserved, charge ordered non-collinear
ferrimagnetic behavior with a considerably high magnetization $\textbf{M}$
coexisting with a ferroelectric (FE) order with an electric polarization
$\textbf{P}$ and a strong cross coupling between them which is primarily driven
by the inter-sublattice DM interactions. The key to realize these effects is
the coupling between these microscopic interactions and the FE primary order
parameter. We predict microscopic mechanisms to achieve electric field
$\textbf{E}$ induced spin-reorientation transitions and 180$^{\circ}$ switching
of the direction of $\textbf{M}$. This model was realized in the hexagonal
phase of LuFeO$_3$ doped with electrons. This system shows $P \sim$ 15
$\mu$C/cm$^2$, $M \sim$ 1.3 $\mu_B$/Fe and magnetic transition near room
temperature ($\sim$ 290 K). Our theoretical results are expected to stimulate
further quest for energy-efficient routes to control magnetism for spintronics
applications.


title: Resolution of Singularities in Arbitrary Characteristic

abstract:
  Let $X$ be an integral affine or projective scheme of finite presentation
over a perfect field. We prove that $X$ admits a resolution, that is, there
exists a smooth scheme $\widetilde X$ and a projective birational morphism from
$\widetilde X$ onto $X$.


title: Self-supervised Social Relation Representation for Human Group Detection

abstract:
  Human group detection, which splits crowd of people into groups, is an
important step for video-based human social activity analysis. The core of
human group detection is the human social relation representation and
division.In this paper, we propose a new two-stage multi-head framework for
human group detection. In the first stage, we propose a human behavior
simulator head to learn the social relation feature embedding, which is
self-supervisely trained by leveraging the socially grounded multi-person
behavior relationship. In the second stage, based on the social relation
embedding, we develop a self-attention inspired network for human group
detection. Remarkable performance on two state-of-the-art large-scale
benchmarks, i.e., PANDA and JRDB-Group, verifies the effectiveness of the
proposed framework. Benefiting from the self-supervised social relation
embedding, our method can provide promising results with very few (labeled)
training data. We will release the source code to the public.


title:
Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution
  Networks

abstract:
  Light-weight super-resolution (SR) models have received considerable
attention for their serviceability in mobile devices. Many efforts employ
network quantization to compress SR models. However, these methods suffer from
severe performance degradation when quantizing the SR models to ultra-low
precision (e.g., 2-bit and 3-bit) with the low-cost layer-wise quantizer. In
this paper, we identify that the performance drop comes from the contradiction
between the layer-wise symmetric quantizer and the highly asymmetric activation
distribution in SR models. This discrepancy leads to either a waste on the
quantization levels or detail loss in reconstructed images. Therefore, we
propose a novel activation quantizer, referred to as Dynamic Dual Trainable
Bounds (DDTB), to accommodate the asymmetry of the activations. Specifically,
DDTB innovates in: 1) A layer-wise quantizer with trainable upper and lower
bounds to tackle the highly asymmetric activations. 2) A dynamic gate
controller to adaptively adjust the upper and lower bounds at runtime to
overcome the drastically varying activation ranges over different samples.To
reduce the extra overhead, the dynamic gate controller is quantized to 2-bit
and applied to only part of the SR networks according to the introduced dynamic
intensity. Extensive experiments demonstrate that our DDTB exhibits significant
performance improvements in ultra-low precision. For example, our DDTB achieves
a 0.70dB PSNR increase on Urban100 benchmark when quantizing EDSR to 2-bit and
scaling up output images to x4. Code is at
\url{https://github.com/zysxmu/DDTB}.


title:
Topological phase transition in magnon bands in a honeycomb ferromagnet
  driven by sublattice symmetry breaking

abstract:
  Ferromagnetic honeycomb systems are known to exhibit a magnonic topological
phase under the existence of the next-nearest neighbor Dzyaloshinskii-Moriya
interaction (DMI). Motivated by the recent progress in the sublattice-specific
control of magnetic anisotropy, we study the topological phase of magnon bands
of honeycomb ferromagnetic monolayer and bilayer with the sublattice symmetry
breaking due to the different anisotropy energy in the presence of the DMI. We
show that there is a topological phase transition between the topological
magnon insulator and the topologically trivial magnon phase driven by the
change of the relative size of the DMI and the anisotropy differences between
the sublattices. The magnon thermal Hall conductivity is proposed as an
experimental probe of the magnon topology.


title: Delaunay decompositions minimizing energy of weighted toroidal graphs

abstract:
  Given a weighted toroidal graph, each realization to a Euclidean torus is
associated with the Dirichlet energy. By minimizing the energy over all
possible Euclidean structures and over all realizations within a fixed homotopy
class, one obtains a harmonic map into an optimal Euclidean torus. We show that
only with this optimal Euclidean structure, the harmonic map and the edge
weights are induced from a weighted Delaunay decomposition.


title: Trust in AI and Implications for the AEC Research: A Literature Analysis

abstract:
  Engendering trust in technically acceptable and psychologically embraceable
systems requires domain-specific research to capture unique characteristics of
the field of application. The architecture, engineering, and construction (AEC)
research community has been recently harnessing advanced solutions offered by
artificial intelligence (AI) to improve project workflows. Despite the unique
characteristics of work, workers, and workplaces in the AEC industry, the
concept of trust in AI has received very little attention in the literature.
This paper presents a comprehensive analysis of the academic literature in two
main areas of trust in AI and AI in the AEC, to explore the interplay between
AEC projects unique aspects and the sociotechnical concepts that lead to trust
in AI. A total of 490 peer-reviewed scholarly articles are analyzed in this
study. The main constituents of human trust in AI are identified from the
literature and are characterized within the AEC project types, processes, and
technologies.