Abstract:
  The Bender-Knuth involutions on semistandard Young tableaux are known to
coincide with the tableau switching on horizontal border strips of two adjacent
letters, together with the swapping of those letters. Motivated by this
coincidence and using the shifted tableau switching due to Choi, Nam and Oh
(2019), we consider a shifted version of the Bender-Knuth involutions and
define a shifted version of the Berenstein-Kirillov group (1995). Similarly to
the classical case, the shifted version of the Berenstein-Kirillov group also
acts on the straight-shaped shifted tableau crystals introduced by Gillespie,
Levinson and Purbhoo (2020), via partial Sch\"utzenberger involutions, thus
coinciding with the action of the cactus group on the same crystal, due to the
author. Following the works of Halacheva (2016, 2020), and Chmutov, Glick and
Pylyavskyy (2020), on the relation between the actions of the
Berenstein-Kirillov group and the cactus group on a crystal of straight-shaped
Young tableaux, we also show that the shifted Berenstein-Kirillov group is
isomorphic to a quotient of the cactus group. Not all the known relations that
hold in the classic Berenstein-Kirillov group need to be satisfied by the
shifted Bender-Knuth involutions, but the ones implying the relations of the
cactus group are verified. Hence, we have an alternative presentation for the
cactus group in terms of the shifted Bender-Knuth involutions. We also use the
shifted growth diagrams due to Thomas and Yong (2016) to provide an alternative
proof concerning the mentioned cactus group action.


Abstract:
  Process-induced defects are the leading cause of discrepancies between
as-designed and as-manufactured additive manufacturing (AM) product behavior.
Especially for metal lattices, the variations in the printed geometry cannot be
neglected. Therefore, the evaluation of the influence of microstructural
variability on their mechanical behavior is crucial for the quality assessment
of the produced structures. Commonly, the as-manufactured geometry can be
obtained by computed tomography (CT). However, to incorporate all
process-induced defects into the numerical analysis is often computationally
demanding. Thus, commonly this task is limited to a predefined set of
considered variations, such as strut size or strut diameter. In this work, a
CT-based binary random field is proposed to generate statistically equivalent
geometries of periodic metal lattices. The proposed random field model in
combination with the Finite Cell Method (FCM), an immersed boundary method,
allows to efficiently evaluate the influence of the underlying microstructure
on the variability of the mechanical behavior of AM products. Numerical
analysis of two lattices manufactured at different scales shows an excellent
agreement with experimental data. Furthermore, it provides a unique insight
into the effects of the process on the occurring geometrical variations and
final mechanical behavior.


Abstract:
  Graphene quantum dots (GQDs) represent single layers up to dozens of graphene
layers smaller than 30 nm. GQDs are newish molecules that have aroused great
interest in research because of their exceptional and manageable optical,
electrical, chemical, and structural properties. In this work, we report
electrostatic potential energy maps, or molecular electrostatic potential
surfaces, illustrate the charge distributions of GQDs three-dimensionally.
Knowledge of the charge distributions can be used to determine how GQDs
interact with one another. To analyze the distribution of molecular charges
accurately, a large number of electrostatic potential energy values must be
calculated. The best way to transmit these data is to visualize them as in the
electrostatic potential map. A ZINDO semi-empirical quantum chemistry method
then imposes the calculated data onto an electron density model of the GQDs
derived from the Schr\"odinger equation. To make the electrostatic potential
energy data of GQDs easy to interpret, a color spectrum, with red as the lowest
electrostatic potential energy value and blue as the highest, is employed to
convey the varying intensities of the electrostatic potential energy values.
The results of the four GQD models suggest that the energy of the ionization
potential lies in a range of -7.20 eV to -5.31 eV and the electron affinity is
-2.65 to -0.24 eV.


Abstract:
  This paper presents a novel architecture for model predictive control (MPC)
based indoor climate control of multi-zone buildings to provide energy
efficiency. Unlike prior works we do not assume the availability of a
high-resolution multi-zone building model, which is challenging to obtain.
Instead, the architecture uses a low-resolution model of the building which is
divided into a small number of "meta-zones" that can be easily identified using
existing data-driven modeling techniques. The proposed architecture is
hierarchical. At the higher level, an MPC controller uses the low-resolution
model to make decisions for the air handling unit (AHU) and the meta-zones.
Since the meta-zones are fictitious, a lower level controller converts the
high-level MPC decisions into commands for the individual zones by solving a
projection problem that strikes a trade-off between two potentially conflicting
goals: the AHU-level decisions made by the MPC are respected while the climate
of the individual zones is maintained within the comfort bounds. The
performance of the proposed controller is assessed via simulations in a
high-fidelity simulation testbed and compared to that of a rule-based
controller that is used in practice. Simulations in multiple weather conditions
show the effectiveness of the proposed controller in terms of energy savings,
climate control, and computational tractability.


Abstract:
  Let $K$ be an imaginary quadratic field, with associated quadratic character
$\alpha$. We construct an analytic $p$-adic $L$-function interpolating the
twisted adjoint $L$-values $L(1, \mathrm{ad}(f) \otimes \alpha)$ as $f$ varies
in a Hida family; these special values are non-critical in the sense of
Deligne. Our approach is based on Greenberg--Stevens' idea of $\Lambda$-adic
modular symbols, which considers cohomology with values in a space of $p$-adic
measures.


Abstract:
  Robot navigation in a safe way for complex and crowded situations is studied
in this work. When facing complex environments with both static and dynamic
obstacles, in existing works unicycle nonholonomic robots are prone to two
extreme behaviors, one is to fall into dead ends formed by obstacles, and the
other is to not complete the navigation task in time due to excessive collision
avoidance.As a result, we propose the R-SARL framework, which is based on a
deep reinforcement learning algorithm and where we augment the reward function
to avoid collisions. In particular, we estimate unsafe interactions between the
robot and obstacles in a look-ahead distance and penalize accordingly, so that
the robot can avoid collisions in advance and reach its destination
safely.Furthermore, we penalize frequent excessive detours to reduce the
timeout and thus improve the efficiency of navigation.We test our method in
various challenging and complex crowd navigation tasks. The results show that
our method improves navigation performance and outperforms state-of-the-art
methods.


Abstract:
  Derivations of Borns Rule are of interest because they tell us what's
connected to what in quantum mechanics if we ever need to change or modify it.


Abstract:
  Terrestrial planets with large water inventories are likely ubiquitous and
will be among the first Earth-sized planets to be characterized with upcoming
telescopes. It has previously been argued that waterworlds-particularly those
possessing more than 1% H$_2$O-experience limited melt production and
outgassing due to the immense pressure overburden of their overlying oceans,
unless subject to high internal heating. But an additional, underappreciated
obstacle to outgassing on waterworlds is the high solubility of volatiles in
high-pressure melts. Here, we investigate this phenomenon and show that
volatile solubilities in melts probably prevent almost all magmatic outgassing
from waterworlds. Specifically, for Earth-like gravity and oceanic crust
composition, oceans or water ice exceeding 10-100 km in depth (0.1-1 GPa)
preclude the exsolution of volatiles from partial melt of silicates. This
solubility limit compounds the pressure overburden effect as large surface
oceans limit both melt production and degassing from any partial melt that is
produced. We apply these calculations to Trappist-1 planets to show that, given
current mass and radius constraints and implied surface water inventories,
Trappist-1f and -1g are unlikely to experience volcanic degassing. While other
mechanisms for interior-surface volatile exchange are not completely excluded,
the suppression of magmatic outgassing simplifies the range of possible
atmospheric evolution trajectories and has implications for interpretation of
ostensible biosignature gases, which we illustrate with a coupled model of
planetary interior-climate-atmosphere evolution.


Abstract:
  For $\beta<\frac13$, we consider $C^\beta(\mathbb{T}^3\times [0,T])$ weak
solutions of the incompressible Euler equations that do not conserve the
kinetic energy. We prove that for such solutions the closed and non-empty set
of singular times $\mathcal{B}$ satisfies $\dim_{\mathcal{H}}(\mathcal{B})\geq
\frac{2\beta}{1-\beta}$. This lower bound on the Hausdorff dimension of the
singular set in time is intrinsically linked to the H\"older regularity of the
kinetic energy and we conjecture it to be sharp. As a first step in this
direction, for every $\beta<\beta'<\frac{1}{3}$ we are able to construct, via a
convex integration scheme, non-conservative $C^\beta(\mathbb{T}^3\times [0,T])$
weak solutions of the incompressible Euler system such that
$\dim_{\mathcal{H}}(\mathcal{B})\leq
\frac{1}{2}+\frac{1}{2}\frac{2\beta'}{1-\beta'}$. The structure of the wild
solutions that we build allows moreover to deduce non-uniqueness of
$C^\beta(\mathbb{T}^3\times [0,T])$ weak solutions of the Cauchy problem for
Euler from every smooth initial datum.


Abstract:
  Road detection or traversability analysis has been a key technique for a
mobile robot to traverse complex off-road scenes. The problem has been mainly
formulated in early works as a binary classification one, e.g. associating
pixels with road or non-road labels. Whereas understanding scenes with
fine-grained labels are needed for off-road robots, as scenes are very diverse,
and the various mechanical performance of off-road robots may lead to different
definitions of safe regions to traverse. How to define and annotate
fine-grained labels to achieve meaningful scene understanding for a robot to
traverse off-road is still an open question. This research proposes a
contrastive learning based method. With a set of human-annotated anchor
patches, a feature representation is learned to discriminate regions with
different traversability, a method of fine-grained semantic segmentation and
mapping is subsequently developed for off-road scene understanding. Experiments
are conducted on a dataset of three driving segments that represent very
diverse off-road scenes. An anchor accuracy of 89.8% is achieved by evaluating
the matching with human-annotated image patches in cross-scene validation.
Examined by associated 3D LiDAR data, the fine-grained segments of visual
images are demonstrated to have different levels of toughness and terrain
elevation, which represents their semantical meaningfulness. The resultant maps
contain both fine-grained labels and confidence values, providing rich
information to support a robot traversing complex off-road scenes.


Abstract:
  This paper presents new machine learning approaches to approximate the
solution of optimal stopping problems. The key idea of these methods is to use
neural networks, where the hidden layers are generated randomly and only the
last layer is trained, in order to approximate the continuation value. Our
approaches are applicable for high dimensional problems where the existing
approaches become increasingly impractical. In addition, since our approaches
can be optimized using a simple linear regression, they are very easy to
implement and theoretical guarantees can be provided. In Markovian examples our
randomized reinforcement learning approach and in non-Markovian examples our
randomized recurrent neural network approach outperform the state-of-the-art
and other relevant machine learning approaches.


Abstract:
  Simple closed curves in the plane can be mapped to nontrivial knots under the
action of origami foldings that allow the paper to self-intersect. We show all
tame knot types may be produced in this manner, motivating the development of a
new knot invariant, the fold number, defined as the minimum number of creases
required to obtain an equivalent knot. We study this invariant, presenting a
bound on the fold number by the diagram stick number as well as a class of
torus knots with constant fold number. We also pursue a characterization of
those foldings which admit nontrivial knots, giving a proof that no "physically
realizable", or proper, foldings can admit nontrivial knots. A number of
questions are posed for further study.


Abstract:
  Terahertz-based nano-networks are emerging as a groundbreaking technology
able to play a decisive role in future medical applications owing to their
ability to precisely quantify figures, such as the viral load in a patient or
to predict sepsis shock or heart attacks before they occur. Due to the
extremely limited size of the devices composing these nano-networks, the use of
the Terahertz (THz) band has emerged as the enabling technology for their
communication. However, the characteristics of the THz band, which strictly
reduce the communication range inside the human body, together with the energy
limitations of nano-nodes make the in-body deployment of nano-nodes a
challenging task. To overcome these problems, we propose a novel in-body
flow-guided nano-network architecture consisting of three different devices: i)
nano-node, ii) nano-router, and iii) bio-sensor. As the performance of this
type of nano-network has not been previously explored, a theoretical framework
capturing all its particularities is derived to properly model its behavior and
evaluate its feasibility in real medical applications. Employing this
analytical model, a thorough sensitivity study of its key parameters is
accomplished. Finally, we analyze the terahertz flow-guided nano-network design
to satisfy the requirements of several medical applications of interest.


Abstract:
  In this paper, an energy-consistent finite difference scheme for the
compressible hydrodynamic and magnetohydrodynamic (MHD) equations is
introduced. For the compressible magnetohydrodynamics, an energy-consistent
finite difference formulation is derived using the product rule for the spatial
difference. The conservation properties of the internal, kinetic, and magnetic
energy equations can be satisfied in the discrete level without explicitly
solving the total energy equation. The shock waves and discontinuities in the
numerical solution are stabilized by nonlinear filtering schemes. An
energy-consistent discretization of the filtering schemes is also derived by
introducing the viscous and resistive heating rates. The resulting
energy-consistent formulation can be implemented with the various kinds of
central difference, nonlinear filtering, and time integration schemes. The
second- and fifth-order schemes are implemented based on the proposed
formulation. The conservation properties and the robustness of the present
schemes are demonstrated via one- and two-dimensional numerical tests. The
proposed schemes successfully handle the most stringent problems in extremely
high Mach number and low beta conditions.


Abstract:
  The high-temperature superconducting cuprates are governed by intertwined
spin, charge, and superconducting orders. While various state-of-the-art
numerical methods have demonstrated that these phases also manifest themselves
in doped Hubbard models, they differ on which is the actual ground state.
Finite cluster methods typically indicate that stripe order dominates while
embedded quantum cluster methods, which access the thermodynamic limit by
treating long-range correlations with a dynamical mean field, conclude that
superconductivity does. Here, we report the observation of fluctuating spin and
charge stripes in the doped single-band Hubbard model using a quantum Monte
Carlo dynamical cluster approximation (DCA) method. By resolving both the
fluctuating spin and charge orders using DCA, we demonstrate for the first time
that they survive in the doped Hubbard model in the thermodynamic limit. This
discovery also provides a new opportunity to study the influence of fluctuating
stripe correlations on the model's pairing correlations within a unified
numerical framework.


Abstract:
  Mispronunciation detection and diagnosis (MDD) is designed to identify
pronunciation errors and provide instructive feedback to guide non-native
language learners, which is a core component in computer-assisted pronunciation
training (CAPT) systems. However, MDD often suffers from the data-sparsity
problem due to that collecting non-native data and the associated annotations
is time-consuming and labor-intensive. To address this issue, we explore a
fully end-to-end (E2E) neural model for MDD, which processes learners' speech
directly based on raw waveforms. Compared to conventional hand-crafted acoustic
features, raw waveforms retain more acoustic phenomena and potentially can help
neural networks discover better and more customized representations. To this
end, our MDD model adopts a co-called SincNet module to take input a raw
waveform and covert it to a suitable vector representation sequence. SincNet
employs the cardinal sine (sinc) function to implement learnable bandpass
filters, drawing inspiration from the convolutional neural network (CNN). By
comparison to CNN, SincNet has fewer parameters and is more amenable to human
interpretation. Extensive experiments are conducted on the L2-ARCTIC dataset,
which is a publicly-available non-native English speech corpus compiled for
research on CAPT. We find that the sinc filters of SincNet can be adapted
quickly for non-native language learners of different nationalities.
Furthermore, our model can achieve comparable mispronunciation detection
performance in relation to state-of-the-art E2E MDD models that take input the
standard handcrafted acoustic features. Besides that, our model also provides
considerable improvements on phone error rate (PER) and diagnosis accuracy.


Abstract:
  To achieve higher coding efficiency, Versatile Video Coding (VVC) includes
several novel components, but at the expense of increasing decoder
computational complexity. These technologies at a low bit rate often create
contouring and ringing effects on the reconstructed frames and introduce
various blocking artifacts at block boundaries. To suppress those visual
artifacts, the VVC framework supports four post-processing filter operations.
The interoperation of these filters introduces extra signaling bits and
eventually becomes overhead at higher resolution video processing. In this
paper, a novel deep learning-based model is proposed for sample adaptive offset
(SAO) nonlinear filtering operation and substantiated the merits of intra-inter
frame quality enhancement. We introduced a variable filter size multi-scale CNN
(MSCNN) to improve the denoising operation and incorporated strided
deconvolution for further computation improvement. We demonstrated that our
deconvolution model can effectively be trained by leveraging the high-frequency
edge features learned in a parallel fashion using feature fusion and residual
learning. The simulation results demonstrate that the proposed method
outperforms the baseline VVC method in BD-BR, BD-PSNR measurements and achieves
an average of 3.762 % bit rate saving on the standard video test sequences.


Abstract:
  Autism spectrum disorder is a developmental disorder characterized by
significant social, communication, and behavioral challenges. Individuals
diagnosed with autism, intellectual, and developmental disabilities (AUIDD)
typically require long-term care and targeted treatment and teaching. Effective
treatment of AUIDD relies on efficient and careful behavioral observations done
by trained applied behavioral analysts (ABAs). However, this process
overburdens ABAs by requiring the clinicians to collect and analyze data,
identify the problem behaviors, conduct pattern analysis to categorize and
predict categorical outcomes, hypothesize responsiveness to treatments, and
detect the effects of treatment plans. Successful integration of digital
technologies into clinical decision-making pipelines and the advancements in
automated decision-making using Artificial Intelligence (AI) algorithms
highlights the importance of augmenting teaching and treatments using novel
algorithms and high-fidelity sensors. In this article, we present an
AI-Augmented Learning and Applied Behavior Analytics (AI-ABA) platform to
provide personalized treatment and learning plans to AUIDD individuals. By
defining systematic experiments along with automated data collection and
analysis, AI-ABA can promote self-regulative behavior using reinforcement-based
augmented or virtual reality and other mobile platforms. Thus, AI-ABA could
assist clinicians to focus on making precise data-driven decisions and increase
the quality of individualized interventions for individuals with AUIDD.


Abstract:
  A graph $G=(V,E)$ with geodesic distance $d(\cdot,\cdot)$ is said to be
resolved by a non-empty subset $R$ of its vertices when, for all vertices $u$
and $v$, if $d(u,r)=d(v,r)$ for each $r\in R$, then $u=v$. The metric dimension
of $G$ is the cardinality of its smallest resolving set. In this manuscript, we
present and investigate the notions of resolvability and metric dimension when
the geodesic distance is truncated with a certain threshold $k$; namely, we
measure distances in $G$ using the metric $d_k(u,v):=\min\{d(u,v),k+1\}$. We
denote the metric dimension of $G$ with respect to $d_k$ as $\beta_k(G)$. We
study the behavior of this quantity with respect to $k$ as well as the diameter
of $G$. We also characterize the truncated metric dimension of paths and cycles
as well as graphs with extreme metric dimension, including graphs of order $n$
such that $\beta_k(G)=n-2$ and $\beta_k(G)=n-1$. We conclude with a study of
various problems related to the truncated metric dimension of trees.


Abstract:
  We revisit the squeezed-limit non-Gaussianity in the single-field
non-attractor inflation models from the viewpoint of the cosmological soft
theorem. In the single-field attractor models, inflaton's trajectories with
different initial conditions effectively converge into a single trajectory in
the phase space, and hence there is only one \emph{clock} degree of freedom
(DoF) in the scalar part. Its long-wavelength perturbations can be absorbed
into the local coordinate renormalization and lead to the so-called
\emph{consistency relation} between $n$- and $(n+1)$-point functions. On the
other hand, if the inflaton dynamics deviates from the attractor behavior, its
long-wavelength perturbations cannot necessarily be absorbed and the
consistency relation is expected not to hold any longer. In this work, we
derive a formula for the squeezed bispectrum including the explicit correction
to the consistency relation, as a proof of its violation in the non-attractor
cases. First one must recall that non-attractor inflation needs to be followed
by attractor inflation in a realistic case. Then, even if a specific
non-attractor phase is effectively governed by a single DoF of phase space
(represented by the exact ultra-slow-roll limit) and followed by a single-DoF
attractor phase, its transition phase necessarily involves two DoF in dynamics
and hence its long-wavelength perturbations cannot be absorbed into the local
coordinate renormalization. Thus, it can affect local physics, even taking
account of the so-called \emph{local observer effect}, as shown by the fact
that the bispectrum in the squeezed limit can go beyond the consistency
relation. More concretely, the observed squeezed bispectrum does not vanish in
general for long-wavelength perturbations exiting the horizon during a
non-attractor phase.


Abstract:
  Our environment, whether at work, in public spaces, or at home, is becoming
more connected, and increasingly responsive. Meal preparation even when it
involves simply heating ready-made food can be perceived as a complex process
for people with disabilities. This research aimed to prototype, using a
co-Design approach a Community Supported Appliance (CSA) by developing a Domain
Specific Language (DSL), precisely created for a semi-automated cooking
process. The DSL was shaped and expressed in the idiom of the users and allowed
the CSA to support independence for users while performing daily cooking
activities.


Abstract:
  Nowadays, more and more clinical trials choose combinational agents as the
intervention to achieve better therapeutic responses. However, dose-finding for
combinational agents is much more complicated than single agent as the full
order of combination dose toxicity is unknown. Therefore, regular phase I
designs are not able to identify the maximum tolerated dose (MTD) of
combinational agents. Motivated by such needs, plenty of novel phase I clinical
trial designs for combinational agents were proposed. With so many available
designs, research that compare their performances, explore parameters' impacts,
and provide recommendations is very limited. Therefore, we conducted a
simulation study to evaluate multiple phase I designs that proposed to identify
single MTD for combinational agents under various scenarios. We also explored
influences of different design parameters. In the end, we summarized the pros
and cons of each design, and provided a general guideline in design selection.


Abstract:
  Erosion, as a key control of landslide dynamics, significantly increases the
destructive power by rapidly amplifying its volume, mobility and impact energy.
Mobility is directly linked to the threat posed by an erosive landslide. No
clear-cut mechanical condition has been presented so far for when, how and how
much energy the erosive landslide gains or loses, resulting in enhanced or
reduced mobility. We pioneer a mechanical model for the energy budget of an
erosive landslide that controls its mobility. A fundamentally new understanding
is that the increased inertia due to the increased mass is related to an
entrainment velocity. With this, the true inertia of an erosive landslide can
be ascertained, making a breakthrough in correctly determining the mobility of
the erosive landslide. Outstandingly, erosion velocity regulates the energy
budget and decides whether the landslide mobility will be enhanced or reduced.
This provides the first-ever explicit mechanical quantification of the state of
erosional energy and a precise description of mobility. This addresses the
long-standing question of why many erosive landslides generate higher mobility,
while others reduce mobility. By introducing three key concepts:
erosion-velocity, entrainment-velocity and energy-velocity, we demonstrate that
erosion and entrainment are essentially different processes. Landslides gain
energy and enhance mobility if the erosion velocity is greater than the
entrainment velocity. We introduce two dimensionless numbers, mobility scaling
and erosion number, delivering explicit measure of mobility. We establish a
mechanism of landslide-propulsion providing the erosion-thrust to the
landslide. Analytically obtained velocity indicates that erosion controls the
landslide dynamics. We also present a full set of dynamical equations in
conservative form which correctly includes the erosion induced net momentum
production.


Abstract:
  We report fabrication of EuSb$_2$ single-crystalline films and investigation
of their quantum transport. First-principles calculations demonstrate that
EuSb$_2$ is a magnetic topological nodal-line semimetal protected by
nonsymmorphic symmetry. Observed Shubnikov-de Haas oscillations with multiple
frequency components exhibit small effective masses and two-dimensional
field-angle dependence even in a 250 nm thick film, further suggesting possible
contributions of surface states. This finding of the high-mobility magnetic
topological semimetal will trigger further investigation of exotic quantum
transport phenomena by controlling magnetic order in topological semimetal
films.


Abstract:
  Self-organization is frequently observed in active collectives, from ant
rafts to molecular motor assemblies. General principles describing
self-organization away from equilibrium have been challenging to identify. We
offer a unifying framework that models the behavior of complex systems as
largely random, while capturing their configuration-dependent response to
external forcing. This allows derivation of a Boltzmann-like principle for
understanding and manipulating driven self-organization. We validate our
predictions experimentally in shape-changing robotic active matter, and outline
a methodology for controlling collective behavior. Our findings highlight how
emergent order depends sensitively on the matching between external patterns of
forcing and internal dynamical response properties, pointing towards future
approaches for design and control of active particle mixtures and
metamaterials.


Abstract:
  This study more complex digital platforms in early stages in the two-sided
market to produce powerful network effects. In this study, I use Transfer
Entropy to look for super users who connect hominids in different networks to
achieve higher network effects in the digital platform in the two-sided market,
which has recently become more complex. And this study also aims to redefine
the decision criteria of product managers by helping them define users with
stronger network effects. With the development of technology, the structure of
the industry is becoming more difficult to interpret and the complexity of
business logic is increasing. This phenomenon is the biggest problem that makes
it difficult for start-ups to challenge themselves. I hope this study will help
product managers create new digital economic networks, enable them to make
prioritized, data-driven decisions, and find users who can be the hub of the
network even in small products.


Abstract:
  The time division multiple access (TDMA) technique has been applied in
automotive multiple-input multiple-output (MIMO) radar. However, it suffers
from the transmit energy loss, and as a result the parameter estimation
performance degradation when the number of transmit elements increases. To
tackle these problem, a transmit beamspace (TB) Doppler division multiple
access (DDMA) approach is proposed. First, a phase modulation matrix with empty
Doppler spectrum is introduced. By exploiting the empty Doppler spectrum, a
test function based on sequential detection is developed to mitigate the
Doppler ambiguity in DDMA waveform. Then, a discrete Fourier transform
(DFT)-based TB in slow-time is formed.The proposed method can achieve waveform
diversity in Doppler domain and generate a TB in slow-time that concentrates
the transmitted power in a fixed spatial region to improve the transmit energy
distribution for automotive MIMO radar, which is favored by medium/long range
radar (MRR/LRR) applications. As compared to the conventional TDMA technique,
the proposed TB DDMA approach can fully exploit the transmission capabilities
of all transmit elements to ensure that the emitted power is efficiently used
and inherits easy implementation. Moreover, the proposed TB DDMA method avoids
the trade-off between the active time for each transmit antenna and the frame
time. Simulation results verify the effectiveness of the proposed TB DDMA
approach for automotive MIMO radar.


Abstract:
  The pore-solid interface and its characteristics play a key role in chemical
interactions between minerals in the solid soil matrix and the liquid in pore
space and, consequently, solute transport in soils. Specific surface area
(SSA), typically measured to characterize the pore-solid interface, depends not
only on the particles size distribution, but also particle shapes and surface
roughness. In this note, we investigate the effects of surface roughness and
probing molecule size on SSA estimation, employ concepts from fractals, and
theoretically estimate specific surface area from particle size distribution
and water retention curve (WRC). The former is used to characterize the
particle sizes and the latter to approximately quantify the pore-solid
interface roughness by determining the surface fractal dimension Ds. To
evaluate our approach, we use five Washington and twenty one Arizona soils for
which both particle size distributions and water retention curves were
accurately measured over a wide range of particle sizes and matric potentials.
Comparison with the experiments show that the proposed method estimates the SSA
reasonably well with root mean square error RMSE = 16.8 and 30.1 m2/g for the
Washington and Arizona datasets, respectively.


Abstract:
  The present work proposes a deep-learning-based approach for the
classification of COVID-19 coughs from non-COVID-19 coughs and that can be used
as a low-resource-based tool for early detection of the onset of such
respiratory diseases. The proposed system uses the ResNet-50 architecture, a
popularly known Convolutional Neural Network (CNN) for image recognition tasks,
fed with the log-Mel spectrums of the audio data to discriminate between the
two types of coughs. For the training and validation of the proposed deep
learning model, this work utilizes the Track-1 dataset provided by the DiCOVA
Challenge 2021 organizers. Additionally, to increase the number of
COVID-positive samples and to enhance variability in the training data, it has
also utilized a large open-source database of COVID-19 coughs collected by the
EPFL CoughVid team. Our developed model has achieved an average validation AUC
of 98.88%. Also, applying this model on the Blind Test Set released by the
DiCOVA Challenge, the system has achieved a Test AUC of 75.91%, Test
Specificity of 62.50%, and Test Sensitivity of 80.49%. Consequently, this
submission has secured 16th position in the DiCOVA Challenge 2021 leader-board.


Abstract:
  We introduce the generalized Heisenberg algebra appropriate for realizations
of the $\mathfrak{gl}(n)$ algebra. Linear realizations of the
$\mathfrak{gl}(n)$ algebra are presented and the corresponding star product,
coproduct of momenta and twist are constructed. The dual realization and dual
$\mathfrak{gl}(n)$ algebra are considered. Finally, we present a general
realization of the $\mathfrak{gl}(n)$ algebra, the corresponding coproduct of
momenta and two classes of twists. These results can be applied to physical
theories on noncommutative spaces of the $\mathfrak{gl}(n)$ type.


Abstract:
  This paper investigates the transient stability of power systems co-dominated
by different types of grid-forming (GFM) devices. Synchronous generators (SGs
and VSGs) and droop-controlled inverters are typical GFM devices in modern
power systems. SGs/VSGs are able to provide inertia while droop-controlled
inverters are generally inertialess. The transient stability of power systems
dominated by homogeneous GFM devices has been extensively studied. Regarding
the hybrid system jointly dominated by heterogeneous GFM devices, the transient
stability is rarely reported. This paper aims to fill this gap. It is found
that the synchronization behavior of the hybrid system can be described by a
second-order motion equation, resembling the swing equation of SGs. Moreover,
two significant differences from conventional power systems are discovered. The
first is that the droop control dramatically enhances the damping effect,
greatly affecting the transient stability region. The second is that the
frequency state variable exhibits a jump at the moment of fault disturbances,
thus impacting the post-fault initial-state location and stability assessment.
The underlying mechanism behind the two new characteristics is clarified and
the impact on the transient stability performance is analyzed and verified. The
findings provide new insights into transient stability of power systems hosting
heterogeneous devices.


Abstract:
  We present DeepMVI, a deep learning method for missing value imputation in
multidimensional time-series datasets. Missing values are commonplace in
decision support platforms that aggregate data over long time stretches from
disparate sources, and reliable data analytics calls for careful handling of
missing data. One strategy is imputing the missing values, and a wide variety
of algorithms exist spanning simple interpolation, matrix factorization methods
like SVD, statistical models like Kalman filters, and recent deep learning
methods. We show that often these provide worse results on aggregate analytics
compared to just excluding the missing data. DeepMVI uses a neural network to
combine fine-grained and coarse-grained patterns along a time series, and
trends from related series across categorical dimensions. After failing with
off-the-shelf neural architectures, we design our own network that includes a
temporal transformer with a novel convolutional window feature, and kernel
regression with learned embeddings. The parameters and their training are
designed carefully to generalize across different placements of missing blocks
and data characteristics. Experiments across nine real datasets, four different
missing scenarios, comparing seven existing methods show that DeepMVI is
significantly more accurate, reducing error by more than 50% in more than half
the cases, compared to the best existing method. Although slower than simpler
matrix factorization methods, we justify the increased time overheads by
showing that DeepMVI is the only option that provided overall more accurate
analytics than dropping missing values.


Abstract:
  The quantum relative entropy is a measure of the distinguishability of two
quantum states, and it is a unifying concept in quantum information theory:
many information measures such as entropy, conditional entropy, mutual
information, and entanglement measures can be realized from it. As such, there
has been broad interest in generalizing the notion to further understand its
most basic properties, one of which is the data processing inequality. The
quantum f-divergence of Petz is one generalization of the quantum relative
entropy, and it also leads to other relative entropies, such as the Petz--Renyi
relative entropies. In this contribution, I introduce the optimized quantum
f-divergence as a related generalization of quantum relative entropy. I prove
that it satisfies the data processing inequality, and the method of proof
relies upon the operator Jensen inequality, similar to Petz's original
approach. Interestingly, the sandwiched Renyi relative entropies are particular
examples of the optimized f-divergence. Thus, one benefit of this approach is
that there is now a single, unified approach for establishing the data
processing inequality for both the Petz--Renyi and sandwiched Renyi relative
entropies, for the full range of parameters for which it is known to hold.


Abstract:
  Dynamics and textures of magnetic domain walls (DWs) may largely alter the
electronic behaviors in a Weyl semimetal system via emergent gauge fields.
However, very little is known about even the basic properties of these domain
walls in Weyl materials. In this work, we imaged the spontaneous magnetization
and magnetic susceptibility of a ferromagnetic (FM) Weyl semimetal CeAlSi using
scanning SQUID microscopy. We observed the ferromagnetic DWs lined-up with the
[100] direction (or other degenerate directions). We also discovered the
coexistence of stable and metastable domain phases, which arise likely due to
magnetoelastic and magnetostriction effects and are expected to be highly
tunable with small strains. We applied an in-plane external field as the CeAlSi
sample was cooled down to below the magnetic phase transition of 8.3K, showing
that the pattern of FM domains is strongly correlated with both the amplitude
and the orientation of the external field even for weak fields of a few Gauss.
The area of stable domains increases with field and reaches maximum when the
field is parallel to the main crystallographic axes of the CeAlSi crystal. Our
results suggest that the manipulation of these heterogeneous phases can provide
a practical way to study the interplay between magnetism and electronic
properties in Weyl systems, and that these systems can even serve as a new
platform for magnetic sensors.


Abstract:
  This research focuses on predicting the demand for air taxi urban air
mobility (UAM) services during different times of the day in various geographic
regions of New York City using machine learning algorithms (MLAs). Several
ride-related factors (such as month of the year, day of the week and time of
the day) and weather-related variables (such as temperature, weather conditions
and visibility) are used as predictors for four popular MLAs, namely, logistic
regression, artificial neural networks, random forests, and gradient boosting.
Experimental results suggest gradient boosting to consistently provide higher
prediction performance. Specific locations, certain time periods and weekdays
consistently emerged as critical predictors.


Abstract:
  A long-lasting belief is that the gravitational stress by the moon would be
responsible for earthquakes because of causing a tidal deformation of Earth's
crust. Even worse, earthquakes are sometimes said to be correlated with
eclipses. We review the origin of this wrong statement and show that the idea
is owed to a fallacious perception of coincidence. In ancient times the two
catastrophes were linked interpreting the announcement of Doomsday, while in
modern times a quasi-scientific essay disseminated such an interrelation
shortly before the theory of tectonics.


Abstract:
  The need for open scientific knowledge graphs is ever increasing. While there
are large repositories of open access articles and free publication indexes,
there are still few free knowledge graphs exposing citation networks, and often
their coverage is partial. Consequently, most evaluation processes based on
citation counts rely on commercial citation databases. Things are changing
thanks to the Initiative for Open Citations (I4OC, https://i4oc.org) and the
Initiative for Open Abstracts (I4OA, https://i4oa.org), whose goal is to
campaign for scholarly publishers to open the reference lists and the other
metadata of their articles. This paper investigates the growth of the open
bibliographic metadata and open citations in two scientific knowledge graphs,
OpenCitations' COCI and Crossref, with an experiment on the Italian National
Scientific Qualification (NSQ), the National process for University Professor
qualification which uses data from commercial indexes. We simulated the
procedure by only using such open data and explored similarities and
differences with the official results. The outcomes of the experiment show that
the amount of open bibliographic metadata and open citation data currently
available in the two scientific knowledge graphs adopted is not yet enough for
obtaining results similar to those provided using commercial databases.


Abstract:
  In this work, we analyze the creation of the discharge asymmetry and the
concomitant formation of the DC self-bias voltage in capacitively coupled radio
frequency plasmas driven by multi-frequency waveforms, as a function of the
electrode surface characteristics. For this latter, we consider and vary the
coefficients that characterize the elastic reflection of the electrons from the
surfaces and the ion-induced secondary electron yield. Our investigations are
based on Particle-in-Cell/Monte Carlo Collision simulations of the plasma and
on a model that aids the understanding of the computational results. Electron
reflection from the electrodes is found to affect slightly the discharge
asymmetry in the presence of multi-frequency excitation, whereas secondary
electrons cause distinct changes to the asymmetry of the plasma as a function
of the phase angle between the harmonics of the driving voltage waveform and as
a function the number of these harmonics.


Abstract:
  End-to-end (E2E) modeling is advantageous for automatic speech recognition
(ASR) especially for Japanese since word-based tokenization of Japanese is not
trivial, and E2E modeling is able to model character sequences directly. This
paper focuses on the latest E2E modeling techniques, and investigates their
performances on character-based Japanese ASR by conducting comparative
experiments. The results are analyzed and discussed in order to understand the
relative advantages of long short-term memory (LSTM), and Conformer models in
combination with connectionist temporal classification, transducer, and
attention-based loss functions. Furthermore, the paper investigates on
effectivity of the recent training techniques such as data augmentation
(SpecAugment), variational noise injection, and exponential moving average. The
best configuration found in the paper achieved the state-of-the-art character
error rates of 4.1%, 3.2%, and 3.5% for Corpus of Spontaneous Japanese (CSJ)
eval1, eval2, and eval3 tasks, respectively. The system is also shown to be
computationally efficient thanks to the efficiency of Conformer transducers.


Abstract:
  We propose a new sampling algorithm combining two quite powerful ideas in the
Markov chain Monte Carlo literature -- adaptive Metropolis sampler and
two-stage Metropolis-Hastings sampler. The proposed sampling method will be
particularly very useful for high-dimensional posterior sampling in Bayesian
models with expensive likelihoods. In the first stage of the proposed
algorithm, an adaptive proposal is used based on the previously sampled states
and the corresponding acceptance probability is computed based on an
approximated inexpensive target density. The true expensive target density is
evaluated while computing the second stage acceptance probability only if the
proposal is accepted in the first stage. The adaptive nature of the algorithm
guarantees faster convergence of the chain and very good mixing properties. On
the other hand, the two-stage approach helps in rejecting the bad proposals in
the inexpensive first stage, making the algorithm computationally efficient. As
the proposals are dependent on the previous states the chain loses its Markov
property, but we prove that it retains the desired ergodicity property. The
performance of the proposed algorithm is compared with the existing algorithms
in two simulated and two real data examples.


Abstract:
  The integration of behavioral phenomena into mechanistic models of cognitive
function is a fundamental staple of cognitive science. Yet, researchers are
beginning to accumulate increasing amounts of data without having the temporal
or monetary resources to integrate these data into scientific theories. We seek
to overcome these limitations by incorporating existing machine learning
techniques into an open-source pipeline for the automated construction of
quantitative models. This pipeline leverages the use of neural architecture
search to automate the discovery of interpretable model architectures, and
automatic differentiation to automate the fitting of model parameters to data.
We evaluate the utility of these methods based on their ability to recover
quantitative models of human information processing from synthetic data. We
find that these methods are capable of recovering basic quantitative motifs
from models of psychophysics, learning and decision making. We also highlight
weaknesses of this framework and discuss future directions for their
mitigation.


Abstract:
  Many advances that have improved the robustness and efficiency of deep
reinforcement learning (RL) algorithms can, in one way or another, be
understood as introducing additional objectives, or constraints, in the policy
optimization step. This includes ideas as far ranging as exploration bonuses,
entropy regularization, and regularization toward teachers or data priors when
learning from experts or in offline RL. Often, task reward and auxiliary
objectives are in conflict with each other and it is therefore natural to treat
these examples as instances of multi-objective (MO) optimization problems. We
study the principles underlying MORL and introduce a new algorithm,
Distillation of a Mixture of Experts (DiME), that is intuitive and
scale-invariant under some conditions. We highlight its strengths on standard
MO benchmark problems and consider case studies in which we recast offline RL
and learning from experts as MO problems. This leads to a natural algorithmic
formulation that sheds light on the connection between existing approaches. For
offline RL, we use the MO perspective to derive a simple algorithm, that
optimizes for the standard RL objective plus a behavioral cloning term. This
outperforms state-of-the-art on two established offline RL benchmarks.


Abstract:
  The first attempt is made to provide a quantitative theoretical
interpretation of the WASA-at-COSY experimental data on the basic double-pion
production reactions $pn \to d \pi^0\pi^0$ and $pn \to d \pi^+\pi^-$ in the
energy region $T_p =1$ - $1.3$ GeV [P. Adlarson et al., Phys. Lett. B 721, 229
(2013)]. The data are analyzed within a model based on production and decay of
an intermediate $I(J^P)=0(3^+)$ dibaryon resonance $\mathcal{D}_{03}$ (denoted
also as $d^*(2380)$). The observed decrease of the near-threshold enhancement
(the so-called ABC effect) in the reaction $pn \to d \pi^+\pi^-$ in comparison
to that in the reaction $pn \to d \pi^0\pi^0$ is explained (at least partially)
to be due to isospin symmetry violation in the two-pion decay of an
intermediate near-threshold scalar $\sigma$ meson emitted from the
$\mathcal{D}_{03}$ dibaryon resonance under conditions of the partial chiral
symmetry restoration.


Abstract:
  Visual navigation is often cast as a reinforcement learning (RL) problem.
Current methods typically result in a suboptimal policy that learns general
obstacle avoidance and search behaviours. For example, in the target-object
navigation setting, the policies learnt by traditional methods often fail to
complete the task, even when the target is clearly within reach from a human
perspective. In order to address this issue, we propose to learn to imagine a
latent representation of the successful (sub-)goal state. To do so, we have
developed a module which we call Foresight Imagination (ForeSIT). ForeSIT is
trained to imagine the recurrent latent representation of a future state that
leads to success, e.g. either a sub-goal state that is important to reach
before the target, or the goal state itself. By conditioning the policy on the
generated imagination during training, our agent learns how to use this
imagination to achieve its goal robustly. Our agent is able to imagine what the
(sub-)goal state may look like (in the latent space) and can learn to navigate
towards that state. We develop an efficient learning algorithm to train ForeSIT
in an on-policy manner and integrate it into our RL objective. The integration
is not trivial due to the constantly evolving state representation shared
between both the imagination and the policy. We, empirically, observe that our
method outperforms the state-of-the-art methods by a large margin in the
commonly accepted benchmark AI2THOR environment. Our method can be readily
integrated or added to other model-free RL navigation frameworks.


Abstract:
  We present a general theory of interpolation error estimates for smooth
functions and inverse inequalities on anisotropic meshes. In our theory, the
error of interpolation is bound in terms of the diameter of a simplex and a
geometric parameter. In the two-dimensional case, our geometric parameter is
equivalent to the circumradius of a triangle. In the three-dimensional case,
our geometric parameter also represents the flatness of a tetrahedron. This
paper also includes corrections to an error in "General theory of interpolation
error estimates on anisotropic meshes" (Japan Journal of Industrial and Applied
Mathematics, 38 (2021) 163-191), in which Theorem 2 was incorrect.


Abstract:
  The nonzero bulk viscosity signals breaking of the scale invariance. We
demonstrate that a disorder in two-dimensional noninteracting electron gas in a
perpendicular magnetic field results in the nonzero disorder-averaged bulk
viscosity. We derive analytic expression for the bulk viscosity within the
self-consistent Born approximation. This residual bulk viscosity provides the
lower bound for the bulk viscosity of 2D interacting electrons at low enough
temperatures.


Abstract:
  Cochlear implants (CIs) are implantable medical devices that can restore the
hearing sense of people suffering from profound hearing loss. The CI uses a set
of electrode contacts placed inside the cochlea to stimulate the auditory nerve
with current pulses. The exact location of these electrodes may be an important
parameter to improve and predict the performance with these devices. Currently
the methods used in clinics to characterize the geometry of the cochlea as well
as to estimate the electrode positions are manual, error-prone and time
consuming. We propose a Markov random field (MRF) model for CI electrode
localization for cone beam computed tomography (CBCT) data-sets. Intensity and
shape of electrodes are included as prior knowledge as well as distance and
angles between contacts. MRF inference is based on slice sampling particle
belief propagation and guided by several heuristics. A stochastic search finds
the best maximum a posteriori estimation among sampled MRF realizations. We
evaluate our algorithm on synthetic and real CBCT data-sets and compare its
performance with two state of the art algorithms. An increase of localization
precision up to 31.5% (mean), or 48.6% (median) respectively, on real CBCT
data-sets is shown.


Abstract:
  We investigate the $\Lambda$-polytopes, a convex-linear structure recently
defined and applied to the classical simulation of quantum computation with
magic states by sampling. There is one such polytope, $\Lambda_n$, for every
number $n$ of qubits. We establish two properties of the family $\{\Lambda_n,
n\in \mathbb{N}\}$, namely (i) Any extremal point (vertex) $A_\alpha \in
\Lambda_m$ can be used to construct vertices in $\Lambda_n$, for all $n>m$.
(ii) For vertices obtained through this mapping, the classical simulation of
quantum computation with magic states can be efficiently reduced to the
classical simulation based on the preimage $A_\alpha$. In addition, we describe
a new class of vertices in $\Lambda_2$ which is outside the known
classification. While the hardness of classical simulation remains an open
problem for most extremal points of $\Lambda_n$, the above results extend
efficient classical simulation of quantum computations beyond the presently
known range.


Abstract:
  GAN inversion aims to invert a given image back into the latent space of a
pretrained GAN model, for the image to be faithfully reconstructed from the
inverted code by the generator. As an emerging technique to bridge the real and
fake image domains, GAN inversion plays an essential role in enabling the
pretrained GAN models such as StyleGAN and BigGAN to be used for real image
editing applications. Meanwhile, GAN inversion also provides insights on the
interpretation of GAN's latent space and how the realistic images can be
generated. In this paper, we provide an overview of GAN inversion with a focus
on its recent algorithms and applications. We cover important techniques of GAN
inversion and their applications to image restoration and image manipulation.
We further elaborate on some trends and challenges for future directions.


Abstract:
  Non-Maximum Suppression (NMS) is essential for object detection and affects
the evaluation results by incorporating False Positives (FP) and False
Negatives (FN), especially in crowd occlusion scenes. In this paper, we raise
the problem of weak connection between the training targets and the evaluation
metrics caused by NMS and propose a novel NMS-Loss making the NMS procedure can
be trained end-to-end without any additional network parameters. Our NMS-Loss
punishes two cases when FP is not suppressed and FN is wrongly eliminated by
NMS. Specifically, we propose a pull loss to pull predictions with the same
target close to each other, and a push loss to push predictions with different
targets away from each other. Experimental results show that with the help of
NMS-Loss, our detector, namely NMS-Ped, achieves impressive results with Miss
Rate of 5.92% on Caltech dataset and 10.08% on CityPersons dataset, which are
both better than state-of-the-art competitors.


Abstract:
  For abelian surfaces of Picard rank 1, we perform explicit computations of
the cohomological rank functions of the ideal sheaf of one point, and in
particular of the basepoint-freeness threshold. Our main tool is the relation
between cohomological rank functions and Bridgeland stability. In virtue of
recent results of Caucci and Ito, these computations provide new information on
the syzygies of polarized abelian surfaces.


Abstract:
  The Zakharov system in dimension $d\leqslant 3$ is shown to be locally
well-posed in Sobolev spaces $H^s \times H^l$, extending the previously known
result. We construct new solution spaces by modifying the $X^{s,b}$ spaces,
specifically by introducing temporal weights. We use contraction mapping
principle to prove local well-posedness in the same. The result obtained is
sharp up to endpoints.


Abstract:
  A common trait involving the opinion dynamics in social networks is an anchor
on interacting network to characterize the opinion formation process among
participating social actors, such as information flow, cooperative and
antagonistic influence, etc. Nevertheless, interacting networks are generally
public for social groups, as well as other individuals who may be interested
in. This blocks a more precise interpretation of the opinion formation process
since social actors always have complex feeling, motivation and behavior, even
beliefs that are personally private. In this paper, we formulate a general
configuration on describing how individual's opinion evolves in a distinct
fashion. It consists of two functional networks: interacting network and
appraisal network. Interacting network inherits the operational properties as
DeGroot iterative opinion pooling scheme while appraisal network, forming a
belief system, quantifies certain cognitive orientation to interested
individuals' beliefs, over which the adhered attitudes may have the potential
to be antagonistic. We explicitly show that cooperative appraisal network
always leads to consensus in opinions. Antagonistic appraisal network, however,
causes opinion cluster. It is verified that antagonistic appraisal network
affords to guarantee consensus by imposing some extra restrictions. They hence
bridge a gap between the consensus and the clusters in opinion dynamics. We
further attain a gauge on the appraisal network by means of the random convex
optimization approach. Moreover, we extend our results to the case of mutually
interdependent issues.


Abstract:
  The multiple Birkhoff recurrence theorem states that for any $d\in\mathbb N$,
every system $(X,T)$ has a multiply recurrent point $x$, i.e. $(x,x,\ldots, x)$
is recurrent under $\tau_d=:T\times T^2\times \ldots \times T^d$. It is natural
to ask if there always is a multiply minimal point, i.e. a point $x$ such that
$(x,x,\ldots,x)$ is $\tau_d$-minimal. A negative answer is presented in this
paper via studying the horocycle flows.
  However, it is shown that for any minimal system $(X,T)$ and any non-empty
open set $U$, there is $x\in U$ such that $\{n\in{\mathbb Z}: T^nx\in U,
\ldots, T^{dn}x\in U\}$ is piecewise syndetic; and that for a PI minimal
system, any $M$-subsystem of $(X^d, \tau_d)$ is minimal.


Abstract:
  We generalize the Hasse invariant of local class field theory to the tame
Brauer group of a higher dimensional local field, and use it to study the
arithmetic of central simple algebras over such fields, which are given {\it a
priori} as tensor products of standard cyclic algebras.
  We also compute the tame Brauer dimension (or {\it period-index bound}) and
the cyclic length of a general henselian-valued field of finite rank and finite
residue field.


Abstract:
  A waveform model for the eccentric binary black holes named SEOBNRE has been
used to analyze the LIGO-Virgo's gravitational wave data by several groups. The
accuracy of this model has been validated by comparing it with numerical
relativity. However, SEOBNRE is a time-domain model, and the efficiency for
generating waveforms is a bottleneck in data analysis. To overcome this
disadvantage, we offer a reduced-order surrogate model for eccentric binary
black holes based on the SEOBNRE waveforms. This surrogate model (SEOBNRE\_S)
can simulate the complete inspiral-merger-ringdown waves with enough accuracy,
covering eccentricities from 0 to 0.25 (0.1), and mass ratio from 1:1 to 5:1
(2:1) for nonspinning (spinning) binaries. The speed of waveform generation is
accelerated about $10^2 \sim 10^3$ times than the original SEOBNRE model.
Therefore SEOBNRE\_S could be helpful in the analysis of LIGO data to find
potential eccentricities.


Abstract:
  In the recent paper arXiv:1807.02721, B. Lawrence and A. Venkatesh develop a
method of proving finiteness theorems in arithmetic geometry by studying the
geometry of families over a base variety. Their results include a new proof of
both the $S$-unit theorem and Faltings' theorem, obtained by constructing and
studying suitable abelian-by-finite families over
$\mathbb{P}^1\setminus\{0,1,\infty\}$ and over an arbitrary curve of genus
$\geq 2$ respectively. In this paper, we apply this strategy to reprove
Siegel's theorem: we construct an abelian-by-finite family on a punctured
elliptic curve to prove finiteness of $S$-integral points on elliptic curves.


Abstract:
  We have adapted the Vera C. Rubin Observatory Legacy Survey of Space and Time
(LSST) Science Pipelines to process data from the Gravitational-Wave Optical
Transient Observer (GOTO) prototype. In this paper, we describe how we used the
Rubin Observatory LSST Science Pipelines to conduct forced photometry
measurements on nightly GOTO data. By comparing the photometry measurements of
sources taken on multiple nights, we find that the precision of our photometry
is typically better than 20~mmag for sources brighter than 16 mag. We also
compare our photometry measurements against colour-corrected PanSTARRS
photometry, and find that the two agree to within 10~mmag (1$\sigma$) for
bright (i.e., $\sim14^{\rm th}$~mag) sources to 200~mmag for faint (i.e.,
$\sim18^{\rm th}$~mag) sources. Additionally, we compare our results to those
obtained by GOTO's own in-house pipeline, {\sc gotophoto}, and obtain similar
results. Based on repeatability measurements, we measure a $5\sigma$ L-band
survey depth of between 19 and 20 magnitudes, depending on observing
conditions. We assess, using repeated observations of non-varying standard SDSS
stars, the accuracy of our uncertainties, which we find are typically
overestimated by roughly a factor of two for bright sources (i.e., $<15^{\rm
th}$~mag), but slightly underestimated (by roughly a factor of 1.25) for
fainter sources ($>17^{\rm th}$~mag). Finally, we present lightcurves for a
selection of variable sources, and compare them to those obtained with the
Zwicky Transient Factory and GAIA. Despite the Rubin Observatory LSST Science
Pipelines still undergoing active development, our results show that they are
already delivering robust forced photometry measurements from GOTO data.


Abstract:
  We consider the problem of an inextensible but flexible fiber advected by a
steady chaotic flow, and ask the simple question whether the fiber can
spontaneously knot itself. Using a 1D Cosserat model, a simple local viscous
drag model and discrete contact forces, we explore the probability of finding
knots at any given time when the fiber is interacting with the ABC class of
flows. The bending rigidity is shown to have a marginal effect compared to that
of increasing the fiber length. Complex knots are formed up to 11 crossings,
but some knots are more probable than others. The finite-time Lyapunov exponent
of the flow is shown to have a positive effect on the knot probability.
Finally, contact forces appear to be crucial since knotted configurations can
remain stable for times much longer than the turnover time of the flow,
something that is not observed when the fiber can freely cross itself.


Abstract:
  We give a survey for the results in [Yeu20a, Yeu20b, Yeu20c], which attempts
to relate the derived categories under general classes of flips and flops. We
indicate how the approach fails because of what appears to be a formal problem.
We give some ideas, and record some failed attempts, to fix this problem. We
also present some new examples.


Abstract:
  Motivated by the need for decentralized learning, this paper aims at
designing a distributed algorithm for solving nonconvex problems with general
linear constraints over a multi-agent network. In the considered problem, each
agent owns some local information and a local variable for jointly minimizing a
cost function, but local variables are coupled by linear constraints. Most of
the existing methods for such problems are only applicable for convex problems
or problems with specific linear constraints. There still lacks a distributed
algorithm for such problems with general linear constraints and under nonconvex
setting. In this paper, to tackle this problem, we propose a new algorithm,
called "proximal dual consensus" (PDC) algorithm, which combines a proximal
technique and a dual consensus method. We build the theoretical convergence
conditions and show that the proposed PDC algorithm can converge to an
$\epsilon$-Karush-Kuhn-Tucker solution within $\mathcal{O}(1/\epsilon)$
iterations. For computation reduction, the PDC algorithm can choose to perform
cheap gradient descent per iteration while preserving the same order of
$\mathcal{O}(1/\epsilon)$ iteration complexity. Numerical results are presented
to demonstrate the good performance of the proposed algorithms for solving a
regression problem and a classification problem over a network where agents
have only partial observations of data features.


Abstract:
  Fall prevalence is high among elderly people, which is challenging due to the
severe consequences of falling. This is why rapid assistance is a critical
task. Ambient assisted living (AAL) uses recent technologies such as 5G
networks and the internet of medical things (IoMT) to address this research
area. Edge computing can reduce the cost of cloud communication, including high
latency and bandwidth use, by moving conventional healthcare services and
applications closer to end-users. Artificial intelligence (AI) techniques such
as deep learning (DL) have been used recently for automatic fall detection, as
well as supporting healthcare services. However, DL requires a vast amount of
data and substantial processing power to improve its performance for the IoMT
linked to the traditional edge computing environment. This research proposes an
effective fall detection framework based on DL algorithms and mobile edge
computing (MEC) within 5G wireless networks, the aim being to empower
IoMT-based healthcare applications. We also propose the use of a deep gated
recurrent unit (DGRU) neural network to improve the accuracy of existing
DL-based fall detection methods. DGRU has the advantage of dealing with
time-series IoMT data, and it can reduce the number of parameters and avoid the
vanishing gradient problem. The experimental results on two public datasets
show that the DGRU model of the proposed framework achieves higher accuracy
rates compared to the current related works on the same datasets.


Abstract:
  Semantic image segmentation aims to obtain object labels with precise
boundaries, which usually suffers from overfitting. Recently, various data
augmentation strategies like regional dropout and mix strategies have been
proposed to address the problem. These strategies have proved to be effective
for guiding the model to attend on less discriminative parts. However, current
strategies operate at the image level, and objects and the background are
coupled. Thus, the boundaries are not well augmented due to the fixed semantic
scenario. In this paper, we propose ObjectAug to perform object-level
augmentation for semantic image segmentation. ObjectAug first decouples the
image into individual objects and the background using the semantic labels.
Next, each object is augmented individually with commonly used augmentation
methods (e.g., scaling, shifting, and rotation). Then, the black area brought
by object augmentation is further restored using image inpainting. Finally, the
augmented objects and background are assembled as an augmented image. In this
way, the boundaries can be fully explored in the various semantic scenarios. In
addition, ObjectAug can support category-aware augmentation that gives various
possibilities to objects in each category, and can be easily combined with
existing image-level augmentation methods to further boost performance.
Comprehensive experiments are conducted on both natural image and medical image
datasets. Experiment results demonstrate that our ObjectAug can evidently
improve segmentation performance.


Abstract:
  An approximate but straight forward projection method to molecular many
alpha-particle states is proposed and the overlap to the shell model space is
determined. The resulting space is in accordance with the shell model, but
still contains states which are not completely symmetric under permutations of
the alpha-particles, which is one reason to call the construction
semi-microscopic. A new contribution is the construction of the 6- and
7-$\alpha$-particle spaces. The errors of the method propagate toward larger
number of alpha-particles and larger shell excitations. In order to show the
effectiveness of the construction proposed, the so obtained spaces are applied,
within an algebraic cluster model, to $^{20}$Ne, $^{24}$Mg and $^{28}$Si, each
treated as a many-alpha-particle system. Former results on $^{12}$C and
$^{16}$O are resumed.


Abstract:
  We report the discovery of a sextuply-eclipsing sextuple star system from
TESS data, TIC 168789840, also known as TYC 7037-89-1, the first known sextuple
system consisting of three eclipsing binaries. The target was observed in
Sectors 4 and 5 during Cycle 1, with lightcurves extracted from TESS Full Frame
Image data. It was also previously observed by the WASP survey and ASAS-SN. The
system consists of three gravitationally-bound eclipsing binaries in a
hierarchical structure of an inner quadruple system with an outer binary
subsystem. Follow-up observations from several different observatories were
conducted as a means of determining additional parameters. The system was
resolved by speckle interferometry with a 0."42 separation between the inner
quadruple and outer binary, inferring an estimated outer period of ~2 kyr. It
was determined that the fainter of the two resolved components is an 8.217 day
eclipsing binary, which orbits the inner quadruple that contains two eclipsing
binaries with periods of 1.570 days and 1.306 days. MCMC analysis of the
stellar parameters has shown that the three binaries of TIC 168789840 are
"triplets", as each binary is quite similar to the others in terms of mass,
radius, and Teff. As a consequence of its rare composition, structure, and
orientation, this object can provide important new insight into the formation,
dynamics, and evolution of multiple star systems. Future observations could
reveal if the intermediate and outer orbital planes are all aligned with the
planes of the three inner eclipsing binaries.


Abstract:
  An $L^2$ version of the classical Denjoy-Carleman theorem regarding
quasi-analytic functions was proved by P. Chernoff on $\mathbb R^n$ using
iterates of the Laplacian. We give a simple proof of this theorem which
generalizes the result on $\mathbb R^n$ for any $p\in [1, 2]$. We then extend
this result to Riemannian symmetric spaces of compact and noncompact type for
$K$-biinvariant functions.


Abstract:
  Consider the complete graph on $n$ vertices. To each vertex assign an Ising
spin that can take the values $-1$ or $+1$. Each spin $i \in [n]=\{1,2,\dots,
n\}$ interacts with a magnetic field $h \in [0,\infty)$, while each pair of
spins $i,j \in [n]$ interact with each other at coupling strength $n^{-1}
J(i)J(j)$, where $J=(J(i))_{i \in [n]}$ are i.i.d. non-negative random
variables drawn from a prescribed probability distribution $\mathcal{P}$. Spins
flip according to a Metropolis dynamics at inverse temperature $\beta \in
(0,\infty)$. We show that there are critical thresholds $\beta_c$ and
$h_c(\beta)$ such that, in the limit as $n\to\infty$, the system exhibits
metastable behaviour if and only if $\beta \in (\beta_c, \infty)$ and $h \in
[0,h_c(\beta))$. Our main result are sharp asymptotics, up to a multiplicative
error $1+o_n(1)$, of the average crossover time from any metastable state to
the set of states with lower free energy. We use standard techniques of the
potential-theoretic approach to metastability. The leading order term in the
asymptotics does not depend on the realisation of $J$, while the correction
terms do. The leading order of the correction term is $\sqrt{n}$ times a
centred Gaussian random variable with a complicated variance depending on
$\beta,h,\mathcal{P}$ and on the metastable state. The critical thresholds
$\beta_c$ and $h_c(\beta)$ depend on $\mathcal{P}$, and so does the number of
metastable states. We derive an explicit formula for $\beta_c$ and identify
some properties of $\beta \mapsto h_c(\beta)$. Interestingly, the latter is not
necessarily monotone, meaning that the metastable crossover may be re-entrant.


Abstract:
  Many numerical schemes for hyperbolic systems require a piecewise polynomial
reconstruction of the cell averaged values, and to simulate perturbed steady
states accurately we require a so called 'well balanced' reconstruction scheme.
For the shallow water system this involves reconstructing in surface elevation,
to which modifications must be made as the fluid depth becomes small to ensure
positivity.
  We investigate the scheme proposed in Skevington (2021) though numerical
experiments, demonstrating its ability to resolve steady and near steady states
at high accuracy. We also present a modification to the scheme which enables
the resolution of slowly moving shocks and dam break problems without
compromising the well balanced property.


Abstract:
  Adaptable, reconfigurable and programmable are key functionalities for the
next generation of silicon-based photonic processors, neural and quantum
networks. Phase change technology offers proven non-volatile electronic
programmability, however the materials used to date have shown prohibitively
high optical losses which are incompatible with integrated photonic platforms.
Here, we demonstrate the capability of the previously unexplored material
Sb$_2$Se$_3$ for ultralow-loss programmable silicon photonics. The favorable
combination of large refractive index contrast and ultralow losses seen in
Sb$_2$Se$_3$ facilitates an unprecedented optical phase control exceeding
10$\pi$ radians in a Mach-Zehnder interferometer. To demonstrate full control
over the flow of light, we introduce nanophotonic digital patterning as a
conceptually new approach at a footprint orders of magnitude smaller than state
of the art interferometer meshes. Our approach enables a wealth of
possibilities in high-density reconfiguration of optical functionalities on
silicon chip.


Abstract:
  We present a new stochastic particle system on networks which describes the
flocking behavior and pattern formation. More precisely, we consider
Cucker-Smale particles with decentralized formation control and multiplicative
noises on symmetric and connected networks. Under suitable assumptions on the
initial configurations and the network structure, we establish time-asymptotic
stochastic flocking behavior and pattern formation of solutions for the
proposed stochastic particle system. Our approach is based on the Lyapunov
functional energy estimates, and it does not require any spectral information
of the graph associated with the network structure.


Abstract:
  This article studies descent theory in the setting of Berkovich spaces. We
give sufficient conditions for a given fibered category over the category of
k-affinoid algebras to be a stack for the Berkovich analogue of the
faithfully-flat topology. We give some applications to the faithfully flat
descent of morphisms and show that some descent data are always effective. We
also show that the property of being algebraic for a morphism between the
analytification of two schemes is a local property for the faithfully-flat
topology.


Abstract:
  We prove that a compact polyhedron $P$ collapses to a subpolyhedron $Q$ if
and only if there exists a piecewise linear free deformation retraction of $P$
onto $Q$.


Abstract:
  In this paper we give an explicit expression for a star product on the super
Minkowski space written in the supertwistor formalism. The big cell of the
super Grassmannian Gr(2|0, 4|1) is identified with the chiral, super Minkowki
space. The super Grassmannian is an homogeneous space under the action of the
complexification SL(4|1) of SU(2,2|1), the superconformal group in dimension 4,
signature (1,3) and supersymmetry N=1. The quantization is done by substituting
the groups and homogeneous spaces by their quantum deformed counterparts. The
calculations are done in Manin's formalism. When we restrict to the big cell we
can compute explicitly an expression for the super star product in the
Minkowski superspace associated to this deformation and the choice of a certain
basis of monomials.


Abstract:
  We prove that the topological type of a normal surface singularity $(X,0)$
provides finite bounds for the multiplicity and polar multiplicity of $(X,0)$,
as well as for the combinatorics of the families of generic hyperplane sections
and of polar curves of the generic plane projections of $(X,0)$. A key
ingredient in our proof is a topological bound of the growth of the Mather
discrepancies of $(X,0)$, which allows us to bound the number of point blowups
necessary to achieve factorization of any resolution of $(X,0)$ through its
Nash transform. This fits in the program of polar explorations, the quest to
determine the generic polar variety of a singular surface germ, to which the
final part of the paper is devoted.


Abstract:
  Fast radio bursts (FRBs) are very short and bright transients visible over
extragalactic distances. The radio pulse undergoes dispersion caused by free
electrons along the line of sight, most of which are associated with the
large-scale structure (LSS). The total dispersion measure therefore increases
with the line of sight and provides a distance estimate to the source. We
present the first measurement of the Hubble constant using the dispersion
measure -- redshift relation of FRBs with identified host counterpart and
corresponding redshift information. A sample of nine currently available FRBs
yields a constraint of $H_0 = 62.3 \pm 9.1 \,\rm{km}
\,\rm{s}^{-1}\,\rm{Mpc}^{-1}$, accounting for uncertainty stemming from the
LSS, host halo and Milky Way contributions to the observed dispersion measure.
The main current limitation is statistical, and we estimate that a few hundred
events with corresponding redshifts are sufficient for a per cent measurement
of $H_0$. This is a number well within reach of ongoing FRB searches. We
perform a forecast using a realistic mock sample to demonstrate that a
high-precision measurement of the expansion rate is possible without relying on
other cosmological probes. FRBs can therefore arbitrate the current tension
between early and late time measurements of $H_0$ in the near future.


Abstract:
  Sentence-level relation extraction (RE) aims at identifying the relationship
between two entities in a sentence. Many efforts have been devoted to this
problem, while the best performing methods are still far from perfect. In this
paper, we revisit two problems that affect the performance of existing RE
models, namely entity representation and noisy or ill-defined labels. Our
improved baseline model, incorporated with entity representations with typed
markers, achieves an F1 of 74.6% on TACRED, significantly outperforms previous
SOTA methods. Furthermore, the presented new baseline achieves an F1 of 91.1%
on the refined Re-TACRED dataset, demonstrating that the pre-trained language
models achieve unexpectedly high performance on this task. We release our code
to the community for future research.


Abstract:
  MnBi$_2$Te$_4$ (MBT) is a promising antiferromagnetic topological insulator
whose films provide access to novel and technologically important topological
phases, including quantum anomalous Hall states and axion insulators. MBT
device behavior is expected to be sensitive to the various collinear and
non-collinear magnetic phases that are accessible in applied magnetic fields.
Here, we use classical Monte Carlo simulations and electronic structure models
to calculate the ground state magnetic phase diagram as well as topological and
optical properties for few layer films with thicknesses up to six septuple
layers. Using magnetic interaction parameters appropriate for MBT, we find that
it is possible to prepare a variety of different magnetic stacking sequences,
some of which have sufficient symmetry to disallow non-reciprocal optical
response and Hall transport coefficients. Other stacking arrangements do yield
large Faraday and Kerr signals, even when the ground state Chern number
vanishes.


Abstract:
  In experiments that study social phenomena, such as peer influence or herd
immunity, the treatment of one unit may influence the outcomes of others. Such
"interference between units" violates traditional approaches for causal
inference, so that additional assumptions are required to model the underlying
social mechanism. We propose an approach that requires no such assumptions,
allowing for interference that is both unmodeled and strong, with confidence
intervals found using only the randomization of treatment. Additionally, the
approach allows for the usage of regression, matching, or weighting, as may
best fit the application at hand. Inference is done by bounding the
distribution of the estimation error over all possible values of the unknown
counterfactual, using an integer program. Examples are shown using a vaccine
trial and two experiments investigating social influence.


Abstract:
  In this Letter, we explore nonrelativistic string solutions in various
subsectors of the $ SU(1,2|3) $ SMT strings that correspond to different spin
groups and satisfy the respective BPS bounds. In particular, we carry out an
explicit analysis on rotating string solutions in the light of recently
proposed SMT limits. We explore newly constructed SMT limits of type IIB
(super) strings on $ AdS_5 \times S^5 $ and estimate the corresponding leading
order stringy corrections near the respective BPS bounds.


Abstract:
  We introduce a sequential learning algorithm to address a robust controller
tuning problem, which in effect, finds (with high probability) a candidate
solution satisfying the internal performance constraint to a chance-constrained
program which has black-box functions. The algorithm leverages ideas from the
areas of randomised algorithms and ordinal optimisation, and also draws
comparisons with the scenario approach; these have all been previously applied
to finding approximate solutions for difficult design problems. By exploiting
statistical correlations through black-box sampling, we formally prove that our
algorithm yields a controller meeting the prescribed probabilistic performance
specification. Additionally, we characterise the computational requirement of
the algorithm with a probabilistic lower bound on the algorithm's stopping
time. To validate our work, the algorithm is then demonstrated for tuning model
predictive controllers on a diesel engine air-path across a fleet of vehicles.
The algorithm successfully tuned a single controller to meet a desired tracking
error performance, even in the presence of the plant uncertainty inherent
across the fleet. Moreover, the algorithm was shown to exhibit a sample
complexity comparable to the scenario approach.


Abstract:
  We classify all fundamental electrically charged thin shells in general
relativity, i.e., static spherically symmetric perfect fluid thin shells with a
Minkowski spacetime interior and a Reissner-Nordstr\"om spacetime exterior,
characterized by the spacetime mass and electric charge. The fundamental shell
can exist in three states, nonextremal, extremal, and overcharged. The
nonextremal state allows the shell to be located such that its radius can be
outside its own gravitational radius, or can be inside its own Cauchy radius.
The extremal state allows the shell to be located such that its radius can be
outside its own gravitational radius, or can be inside it. The overcharged
state allows the shell to be located anywhere. There is a further division, one
has to specify the orientation of the shell, i.e., whether the normal out of
the shell points toward increasing or decreasing radii. There is still a
subdivision in the extremal state when the shell is at the gravitational
radius, in that the shell can approach it from above or from below. The shell
is assumed to be composed of an electrically charged perfect fluid, and the
energy conditions are tested. Carter-Penrose diagrams are drawn for the shell
spacetimes. There are fourteen cases in the classification of the fundamental
shells, namely, nonextremal star shells, nonextremal tension shell black holes,
nonextremal tension shell regular and nonregular black holes, nonextremal
compact shell naked singularities, Majumdar-Papapetrou star shells, extremal
tension shell singularities, extremal tension shell regular and nonregular
black holes, Majumdar-Papapetrou compact shell naked singularities,
Majumdar-Papapetrou shell quasiblack holes, extremal null shell quasinonblack
holes, extremal null shell singularities, Majumdar-Papapetrou null shell
singularities, overcharged star shells, and overcharged compact shell naked
singularities.


Abstract:
  The Stochastic Loewner equation, introduced by Schramm, gives us a powerful
way to study and classify critical random curves and interfaces in
two-dimensional statistical mechanics. New kind of stochastic Loewner equation,
called fractional stochastic Loewner evolution (FSLE), has been proposed for
the first time. Using the fractional time series as the driving function of the
Loewner equation and local fractional integrodifferential operators, we
introduce a large class of fractal curves. We argue that the FSLE curves,
besides the fractal dimension calculations, have crucial differences which
caused by the Hurst index of the driving function. This extension opens a new
way to classify different types of scaling curves based on the Hurst index of
the corresponding driving function. Such formalism appear to be suitable to
deal with the study of a wide range of two-dimensional curves appearing in
statistical mechanics or natural phenomena.


Abstract:
  We present the photometric and spectroscopic analysis of three Type II SNe:
2014cx, 2014cy and 2015cz. SN 2014cx is a conventional Type IIP with a shallow
slope (0.2 mag/50d) and an atypical short plateau ($\sim$86 d). SNe 2014cy and
2015cz show relatively large decline rates (0.88 and 1.64 mag/50d,
respectively) at early times before settling to the plateau phase, unlike the
canonical Type IIP/L SN light curves. All of them are normal luminosity SN II
with an absolute magnitude at mid-plateau of
M$_{V,14cx}^{50}$=$-$16.6$\pm$0.4$\,\rm{mag}$,
M$_{V,14cy}^{50}$=$-$16.5$\,\pm\,$0.2$\,\rm{mag}$ and
M$_{V,15cz}^{50}$=$-$17.4$\,\pm\,$0.3$\,\rm{mag}$. A relatively broad range of
$^{56}$Ni masses is ejected in these explosions (0.027-0.070 M$_\odot$). The
spectra show the classical evolution of Type II SNe, dominated by a blue
continuum with broad H lines at early phases and narrower metal lines with P
Cygni profiles during the plateau. High-velocity H I features are identified in
the plateau spectra of SN 2014cx at 11600 km s$^{-1}$, possibly a sign of
ejecta-circumstellar interaction. The spectra of SN 2014cy exhibit strong
absorption profile of H I similar to normal luminosity events whereas strong
metal lines akin to sub-luminous SNe. The analytical modelling of the
bolometric light curve of the three events yields similar radii for the three
objects within errors (478, 507 and 608 R$_\odot$ for SNe 2014cx, 2014cy and
2015cz, respectively) and a range of ejecta masses (15.0, 22.2 and 18.7
M$_\odot$ for SNe 2014cx, 2014cy and 2015cz), and a modest range of explosion
energies (3.3 - 6.0 foe where 1 foe = 10$^{51}$ erg).


Abstract:
  Compton scattering imaging using high-energy synchrotron x-rays allows the
visualization of the spatio-temporal lithiation state in lithium-ion batteries
probed in-operando. Here, we apply this imaging technique to the commercial
18650-type cylindrical lithium-ion battery. Our analysis of the lineshapes of
the Compton scattering spectra taken from different electrode layers reveals
the emergence of inhomogeneous lithiation patterns during the charge-discharge
cycles. Moreover, these patterns exhibit oscillations in time where the
dominant period corresponds to the time scale of the charging curve.


Abstract:
  Classifiers tend to propagate biases present in the data on which they are
trained. Hence, it is important to understand how the demographic identities of
the annotators of comments affect the fairness of the resulting model. In this
paper, we focus on the differences in the ways men and women annotate comments
for toxicity, investigating how these differences result in models that amplify
the opinions of male annotators. We find that the BERT model as-sociates toxic
comments containing offensive words with male annotators, causing the model to
predict 67.7% of toxic comments as having been annotated by men. We show that
this disparity between gender predictions can be mitigated by removing
offensive words and highly toxic comments from the training data. We then apply
the learned associations between gender and language to toxic language
classifiers, finding that models trained exclusively on female-annotated data
perform 1.8% better than those trained solely on male-annotated data and that
training models on data after removing all offensive words reduces bias in the
model by 55.5% while increasing the sensitivity by 0.4%.


Abstract:
  We study leptonic CP and flavor violations in supersymmetric (SUSY) grand
unified theory (GUT) with right handed neutrinos, paying attention to the
renormalization group effects on the slepton mass matrices due to the neutrino
and GUT Yukawa interactions. In particular, we study in detail the impacts of
the so-called Casas- Ibarra parameters on CP and flavor violating observables.
The renormalization group effects induce CP and flavor violating elements of
the SUSY breaking scalar mass squared matrices, which may result in sizable
leptonic CP and flavor violating signals. Assuming seesaw formula for the
active neutrino masses, the renormalization group effects have been often
thought to be negligible as the right-handed neutrino masses become small. With
the most general form of the neutrino Yukawa matrix, i.e., taking into account
the Casas-Ibarra parameters, however, this is not the case. We found that the
maximal possible sizes of signals of leptonic CP and flavor violating processes
are found to be insensitive to the mass scale of the right-handed neutrinos and
that they are as large as (or larger than) the present experimental bounds
irrespective of the right-handed neutrino masses.


Abstract:
  Resonant transmission of light is a surface-wave assisted phenomenon that
enables funneling light through subwavelength apertures milled in otherwise
opaque metallic screens. In this work, we introduce a deep learning approach to
efficiently compute and design the optical response of a single subwavelength
slit perforated in a metallic screen and surrounded by periodic arrangements of
indentations. First, we show that a semi-analytical framework based on a
coupled-mode theory formalism is a robust and efficient method to generate the
large training datasets required in the proposed approach. Second, we discuss
how simple, densely connected artificial neural networks can accurately learn
the mapping from the geometrical parameters defining the topology of the system
to its corresponding transmission spectrum. Finally, we report on a deep
learning tandem architecture able to perform inverse design tasks for the
considered class of systems. We expect this work to stimulate further work on
the application of deep learning to the analysis of light-matter interaction in
nanostructured metallic films.


Abstract:
  Given an on-board diagnostics (OBD) dataset and a physics-based emissions
prediction model, this paper aims to develop an accurate and
computational-efficient AI (Artificial Intelligence) method that predicts
vehicle emissions. The problem is of societal importance because vehicular
emissions lead to climate change and impact human health. This problem is
challenging because the OBD data does not contain enough parameters needed by
high-order physics models. Conversely, related work has shown that low-order
physics models have poor predictive accuracy when using available OBD data.
This paper uses a divergent window co-occurrence pattern detection method to
develop a spatiotemporal variability-aware AI model for predicting emission
values from the OBD datasets. We conducted a case study using real-world OBD
data from a local public transportation agency. Results show that the proposed
AI method has approximately 65% improved predictive accuracy than a non-AI
low-order physics model and is approximately 35% more accurate than a baseline
model.


Abstract:
  The present work demonstrates a robust protocol for probing localized
electronic structure in condensed-phase systems, operating in terms of a
recently proposed theory for decomposing the results of Kohn-Sham density
functional theory in a basis of spatially localized molecular orbitals
[Eriksen, J. Chem. Phys. 153, 214109 (2020)]. In an initial application to
liquid, ambient water and the assessment of the solvation energy and the
embedded dipole moment of H$_2$O in solution, we find that both properties are
amplified on average -- in accordance with expectation -- and that correlations
are indeed observed to exist between them. However, the simulated
solvent-induced shift to the dipole moment of water is found to be
significantly dampened with respect to typical literature values. The local
nature of our methodology has further allowed us to evaluate the convergence of
bulk properties with respect to the extent of the underlying one-electron basis
set, ranging from single-$\zeta$ to full (augmented) quadruple-$\zeta$ quality.
Albeit a pilot example, our work paves the way towards future studies of local
effects and defects in more complex phases, e.g., liquid mixtures and even
solid-state crystals.


Abstract:
  In a complex community, species continuously adapt to each other. On rare
occasions, the adaptation of a species can lead to the extinction of others,
and even its own. "Adaptive dynamics" is the standard mathematical framework to
describe evolutionary changes in community interactions, and in particular,
predict adaptation driven extinction. Unfortunately, most authors implement the
equations of adaptive dynamics through computer simulations, that require
assuming a large number of questionable parameters and fitness functions. In
this study we present analytical solutions to adaptive dynamics equations,
thereby clarifying how outcomes depend on any computational input. We develop
general formulas that predict equilibrium abundances over evolutionary time
scales. Additionally, we predict which species will go extinct next, and when
this will happen.


Abstract:
  We obtain an estimate for the cubic Weyl sum which improves the bound
obtained from Weyl differencing for short ranges of summation. In particular,
we show that for any $\varepsilon>0$ there exists some $\delta>0$ such that for
any coprime integers $a,q$ and real number $\gamma$ we have \begin{align*}
\sum_{1\le n \le N}e\left(\frac{an^3}{q}+\gamma n\right)\ll (qN)^{1/4}
q^{-\delta}, \end{align*} provided $q^{1/3+\varepsilon}\le N \le
q^{1/2-\varepsilon}$. Our argument builds on some ideas of Enflo.


Abstract:
  We show that every irreducible integral Apollonian packing can be set in the
Euclidean space so that all of its tangency spinors and all reduced coordinates
and co-curvatures are integral. As a byproduct, we prove that in any integral
Descartes configuration, the sum of the curvatures of two adjacent disks can be
written as a sum of two squares. Descartes groups are defined, and an
interesting occurrence of the Fibonacci sequence is found.


Abstract:
  Neural networks are prone to learning shortcuts -- they often model simple
correlations, ignoring more complex ones that potentially generalize better.
Prior works on image classification show that instead of learning a connection
to object shape, deep classifiers tend to exploit spurious correlations with
low-level texture or the background for solving the classification task. In
this work, we take a step towards more robust and interpretable classifiers
that explicitly expose the task's causal structure. Building on current
advances in deep generative modeling, we propose to decompose the image
generation process into independent causal mechanisms that we train without
direct supervision. By exploiting appropriate inductive biases, these
mechanisms disentangle object shape, object texture, and background; hence,
they allow for generating counterfactual images. We demonstrate the ability of
our model to generate such images on MNIST and ImageNet. Further, we show that
the counterfactual images can improve out-of-distribution robustness with a
marginal drop in performance on the original classification task, despite being
synthetic. Lastly, our generative model can be trained efficiently on a single
GPU, exploiting common pre-trained models as inductive biases.