Abstract:
  Checkpoint/restart (C/R) provides fault-tolerant computing capability,
enables long running applications, and provides scheduling flexibility for
computing centers to support diverse workloads with different priority. It is
therefore vital to get transparent C/R capability working at NERSC. MANA, by
Garg et. al., is a transparent checkpointing tool that has been selected due to
its MPI-agnostic and network-agnostic approach. However, originally written as
a proof-of-concept code, MANA was not ready to use with NERSC's diverse
production workloads, which are dominated by MPI and hybrid MPI+OpenMP
applications. In this talk, we present ongoing work at NERSC to enable MANA for
NERSC's production workloads, including fixing bugs that were exposed by the
top applications at NERSC, adding new features to address system changes,
evaluating C/R overhead at scale, etc. The lessons learned from making MANA
production-ready for HPC applications will be useful for C/R tool developers,
supercomputing centers and HPC end-users alike.


Abstract:
  In this paper, we first establish a version of multidimensional analogues of
the refined Bohr's inequality. Then we establish two versions of
multidimensional analogues of improved Bohr's inequality with initial
coefficient being zero. Finally we establish two versions of multidimensional
analogues of improved Bohr's inequality with the initial coefficient being
replaced by absolute value of the function, and to prove that most of the
results are sharp.


Abstract:
  Cancer registries collect multisource data and provide valuable information
that can lead to unique research opportunities. In the Netherlands, a registry
and model-based approach (MBA) are used for the selection of patients that are
eligible for proton therapy. We collected baseline characteristics including
demographic, clinical, tumour and treatment information. These data were
transformed into a machine readable format using the FAIR (Findable,
Accessible, Interoperable, Reusable) data principles and resulted in a
knowledge graph with baseline characteristics of proton therapy patients. With
this approach, we enable the possibility of linking external data sources and
optimal flexibility to easily adapt the data structure of the existing
knowledge graph to the needs of the clinic.


Abstract:
  There has recently been significant interest in training reinforcement
learning (RL) agents in vision-based environments. This poses many challenges,
such as high dimensionality and the potential for observational overfitting
through spurious correlations. A promising approach to solve both of these
problems is an attention bottleneck, which provides a simple and effective
framework for learning high performing policies, even in the presence of
distractions. However, due to poor scalability of attention architectures,
these methods cannot be applied beyond low resolution visual inputs, using
large patches (thus small attention matrices). In this paper we make use of new
efficient attention algorithms, recently shown to be highly effective for
Transformers, and demonstrate that these techniques can be successfully adopted
for the RL setting. This allows our attention-based controllers to scale to
larger visual inputs, and facilitate the use of smaller patches, even
individual pixels, improving generalization. We show this on a range of tasks
from the Distracting Control Suite to vision-based quadruped robots locomotion.
We provide rigorous theoretical analysis of the proposed algorithm.


Abstract:
  Privacy preservation remains a key challenge in data mining and Natural
Language Understanding (NLU). Previous research shows that the input text or
even text embeddings can leak private information. This concern motivates our
research on effective privacy preservation approaches for pretrained Language
Models (LMs). We investigate the privacy and utility implications of applying
dx-privacy, a variant of Local Differential Privacy, to BERT fine-tuning in NLU
applications. More importantly, we further propose privacy-adaptive LM
pretraining methods and show that our approach can boost the utility of BERT
dramatically while retaining the same level of privacy protection. We also
quantify the level of privacy preservation and provide guidance on privacy
configuration. Our experiments and findings lay the groundwork for future
explorations of privacy-preserving NLU with pretrained LMs.


Abstract:
  Classical main-sequence chemically peculiar stars show light variability that
originates in surface abundance spots. In the spots, the flux redistribution
due to line (bound-bound) and bound-free transitions is modulated by stellar
rotation and leads to light variability. White dwarfs and hot subdwarfs may
also have surface abundance spots either owing to the elemental diffusion or as
a result of accretion of debris. We model the light variability of typical
white dwarfs and hot subdwarfs that results from putative surface abundance
spots. We show that the spots with radiatively supported iron overabundance may
cause observable light variability of hot white dwarfs and subdwarfs. Accretion
of debris material may lead to detectable light variability in warm white
dwarfs. We apply our model to the helium star HD 144941 and conclude that the
spot model is able to explain most of observed light variations of this star.


Abstract:
  We propose a new model, DocHopper, that iteratively attends to different
parts of long, hierarchically structured documents to answer complex questions.
Similar to multi-hop question-answering (QA) systems, at each step, DocHopper
uses a query $q$ to attend to information from a document, combines this
``retrieved'' information with $q$ to produce the next query. However, in
contrast to most previous multi-hop QA systems, DocHopper is able to
``retrieve'' either short passages or long sections of the document, thus
emulating a multi-step process of ``navigating'' through a long document to
answer a question. To enable this novel behavior, DocHopper does not combine
document information with $q$ by concatenating text to the text of $q$, but by
combining a compact neural representation of $q$ with a compact neural
representation of a hierarchical part of the document, which can potentially be
quite large. We experiment with DocHopper on four different QA tasks that
require reading long and complex documents to answer multi-hop questions, and
show that DocHopper achieves state-of-the-art results on three of the datasets.
Additionally, DocHopper is efficient at inference time, being 3--10 times
faster than the baselines.


Abstract:
  Streaming 360{\deg} video demands high bandwidth and low latency, and poses
significant challenges to Internet Service Providers (ISPs) and Mobile Network
Operators (MNOs). The identification of 360{\deg} video traffic can therefore
benefits fixed and mobile carriers to optimize their network and provide better
Quality of Experience (QoE) to the user. However, end-to-end encryption of
network traffic has obstructed identifying those 360{\deg} videos from regular
videos. As a solution this paper presents 360NorVic, a near-realtime and
offline Machine Learning (ML) classification engine to distinguish 360{\deg}
videos from regular videos when streamed from mobile devices. We collect packet
and flow level data for over 800 video traces from YouTube & Facebook
accounting for 200 unique videos under varying streaming conditions. Our
results show that for near-realtime and offline classification at packet level,
average accuracy exceeds 95%, and that for flow level, 360NorVic achieves more
than 92% average accuracy. Finally, we pilot our solution in the commercial
network of a large MNO showing the feasibility and effectiveness of 360NorVic
in production settings.


Abstract:
  It is a well-known fact that the non-pluripolar self-products of a closed
positive (1,1)-current in a big nef cohomology class on a compact Kahler
manifold are not of full mass in the presence of positive Lelong numbers of the
current in consideration. In this paper, we give a quantitative version of the
last property. Our proof involves a generalization of Demailly's comparison of
Lelong numbers to the setting of density currents, a reversed
Alexandrov-Fenchel inequality, and the notion of relative non-pluripolar
products.


Abstract:
  We present a novel redirected walking controller based on alignment that
allows the user to explore large and complex virtual environments, while
minimizing the number of collisions with obstacles in the physical environment.
Our alignment-based redirection controller, ARC, steers the user such that
their proximity to obstacles in the physical environment matches the proximity
to obstacles in the virtual environment as closely as possible. To quantify a
controller's performance in complex environments, we introduce a new metric,
Complexity Ratio (CR), to measure the relative environment complexity and
characterize the difference in navigational complexity between the physical and
virtual environments. Through extensive simulation-based experiments, we show
that ARC significantly outperforms current state-of-the-art controllers in its
ability to steer the user on a collision-free path. We also show through
quantitative and qualitative measures of performance that our controller is
robust in complex environments with many obstacles. Our method is applicable to
arbitrary environments and operates without any user input or parameter
tweaking, aside from the layout of the environments. We have implemented our
algorithm on the Oculus Quest head-mounted display and evaluated its
performance in environments with varying complexity. Our project website is
available at https://gamma.umd.edu/arc/.


Abstract:
  I introduce a modification of continuous matrix product states (CMPS) that
makes them adapted to relativistic quantum field theories (QFT). These
relativistic CMPS can be used to solve genuine 1+1 dimensional QFT without UV
cutoff and directly in the thermodynamic limit. The main idea is to work
directly in the basis that diagonalizes the free part of the model considered,
which allows to fit its short distance behavior exactly. This makes
computations slightly less trivial than with standard CMPS. However, they
remain feasible and I present all the steps needed for the optimization. The
asymptotic cost as a function of the bond dimension remains the same as for
standard CMPS. I illustrate the method on the self-interacting scalar field,
a.k.a. the $\phi^4_2$ model. Aside from providing unequaled precision in the
continuum, the numerical results obtained are truly variational, and thus
provide rigorous energy upper bounds.


Abstract:
  Relatively dominated representations give a common generalization of
geometrically finiteness in rank one on the one hand, and the Anosov condition
which serves as a higher-rank analogue of convex cocompactness on the other.
This note proves three results about these representations.
  Firstly, we remove the quadratic gaps assumption involved in the original
definition. Secondly, we give a characterization using eigenvalue gaps,
providing a relative analogue of a result of Kassel--Potrie for Anosov
representations. Thirdly, we formulate characterizations in terms of singular
value or eigenvalue gaps combined with limit maps, in the spirit of
Gu\'eritaud--Guichard--Kassel--Wienhard for Anosov representations, and use
them to show that inclusion representations of certain groups playing weak
ping-pong and positive representations in the sense of Fock--Goncharov are
relatively dominated.


Abstract:
  I summarize recent developments in electroweak precision physics and global
fits. Expectations for future measurements, both at lower energies and the
energy frontier, are also discussed.


Abstract:
  Metal halide perovskite semiconductors have outstanding optoelectronic
properties. Although these perovskites are defect-tolerant electronically,
defects hamper their long-term stability and cause degradation. Density
functional theory (DFT) calculations are an important tool to unravel the
microscopic structures of defects, but results suffer from the different
approximations used in the DFT functionals. In the case of metal halide
perovskites, qualitatively different results have been reported with different
functionals, either predicting vacancy or interstitial point defects to be most
dominant. Here, we conduct a comprehensive comparison of a wide range of
functionals for calculating the equilibrium defect formation energies and
concentrations of point defects in the archetype metal halide perovskite,
MAPbI$_3$. We find that it is essential to include long-range Van der Waals
interactions in the functional, and that it is vital to self-consistently
optimize structure and volume of all compounds involved in the defect
formation. For calculating equilibrium formation energies of point defects in
MAPbI$_3$ and similar metal halide perovskites, we argue that the exact values
of the chemical potentials of the species involved, or of the intrinsic Fermi
level, are not important. In contrast to the simple Schottky or Frenkel
pictures, we find that the dominant defects are MA and I interstitials, and Pb
vacancies.


Abstract:
  We cross-match the Alma catalog of OB stars with Gaia DR2 astrometry and
photometry as a first step towards producing a clean sample of massive stars in
the solar neighbourhood with a high degree of completeness. We analyze the
resulting colour-absolute magnitude diagram to divide our sample into
categories and compare extinction estimates from two sources, finding problems
with both of them. The distances obtained with three different priors are found
to have few differences among them, indicating that Gaia DR2 distances are
robust. An analysis of the 3-D distribution of massive stars in the solar
neighbourhood is presented. We show that a kinematically distinct structure we
dub the Cepheus spur extends from the Orion-Cygnus spiral arm towards the
Perseus arm and is located above the Galactic mid-plane, likely being related
to the recently discovered Radcliffe wave. We propose that this corrugation
pattern in the Galactic disk may be responsible for the recent enhanced star
formation at its crests and troughs. We also discuss our plans to extend this
work in the immediate future.


Abstract:
  A pandemic caused by a new coronavirus (COVID-19) has spread worldwide,
inducing an epidemic still active in Argentina. In this chapter, we present a
case study using an SEIR (Susceptible-Exposed-Infected-Recovered) diffusion
model of fractional order in time to analyze the evolution of the epidemic in
Buenos Aires and neighboring areas (Regi\'on Metropolitana de Buenos Aires,
(RMBA)) comprising about 15 million inhabitants. In the SEIR model, individuals
are divided into four classes, namely, susceptible (S), exposed (E), infected
(I) and recovered (R). The SEIR model of fractional order allows for the
incorporation of memory, with hereditary properties of the system, being a
generalization of the classic SEIR first-order system, where such effects are
ignored. Furthermore, the fractional model provides one additional parameter to
obtain a better fit of the data. The parameters of the model are calibrated by
using as data the number of casualties officially reported. Since infinite
solutions honour the data, we show a set of cases with different values of the
lockdown parameters, fatality rate, and incubation and infectious periods. The
different reproduction ratios R0 and infection fatality rates (IFR) so obtained
indicate the results may differ from recent reported values, constituting
possible alternative solutions. A comparison with results obtained with the
classic SEIR model is also included. The analysis allows us to study how
isolation and social distancing measures affect the time evolution of the
epidemic.


Abstract:
  Lazy search algorithms have been developed to efficiently solve planning
problems in domains where the computational effort is dominated by the cost of
edge evaluation. The existing algorithms operate by intelligently balancing
computational effort between searching the graph and evaluating edges. However,
they are designed to run as a single process and do not leverage the
multithreading capability of modern processors. In this work, we propose a
massively parallelized, bounded suboptimal, lazy search algorithm (MPLP) that
harnesses modern multi-core processors. In MPLP, searching of the graph and
edge evaluations are performed completely asynchronously in parallel, leading
to a drastic improvement in planning time. We validate the proposed algorithm
in two different planning domains: 1) motion planning for 3D humanoid
navigation and 2) task and motion planning for a robotic assembly task. We show
that MPLP outperforms the state of the art lazy search as well as parallel
search algorithms.


Abstract:
  We introduce polynomial couplings, a generalization of probabilistic
couplings, to develop an algorithm for the computation of equivalence relations
which can be interpreted as a lifting of probabilistic bisimulation to
polynomial differential equations, a ubiquitous model of dynamical systems
across science and engineering. The algorithm enjoys polynomial time complexity
and complements classical partition-refinement approaches because: (a) it
implements a local exploration of the system, possibly yielding equivalences
that do not necessarily involve the inspection of the whole system of
differential equations; (b) it can be enhanced by up-to techniques; and (c) it
allows the specification of pairs which ought not to be included in the output.
Using a prototype, these advantages are demonstrated on case studies from
systems biology for applications to model reduction and comparison. Notably, we
report four orders of magnitude smaller runtimes than partition-refinement
approaches when disproving equivalences between Markov chains.


Abstract:
  Chalcogenide perovskites have emerged as a new class of electronic materials,
but fundamental properties and applications of chalcogenide perovskites remain
limited by the lack of high quality epitaxial thin films. We report epitaxial
thin film growth of BaZrS3, a prototypical chalcogenide, by pulsed laser
deposition. X-ray diffraction studies show that the films are strongly textured
out of plane and have a clear in-plane epitaxial relationship with the
substrate. Electron microscopy studies confirm the presence of epitaxy for the
first few layers of the film at the interface, even though away from the
interface the films are polycrystalline with a large number of extended defects
suggesting the potential for further improvement in growth. X-Ray reflectivity
and atomic force microscopy show smooth film surfaces and interfaces between
the substrate and the film. The films show strong light absorption near the
band edge and photoluminescence in the visible region. The photodetector
devices show fast and efficient photo response with the highest ON/OFF ratio
reported for BaZrS3 films thus far. Our study opens up opportunities to realize
epitaxial thin films, heterostructures, and superlattices of chalcogenide
perovskites to probe fundamental physical phenomena and the resultant
electronic and photonic device technologies.


Abstract:
  Using a high degree of parallelism is essential to perform data assimilation
efficiently. The state formulation of the incremental weak constraint
four-dimensional variational data assimilation method allows parallel
calculations in the time dimension. In this approach, the solution is
approximated by minimising a series of quadratic cost functions using the
conjugate gradient method. To use this method in practice, effective
preconditioning strategies that maintain the potential for parallel
calculations are needed. We examine approximations to the control variable
transform (CVT) technique when the latter is beneficial. The new strategy
employs a randomised singular value decomposition and retains the potential for
parallelism in the time domain. Numerical results for the Lorenz 96 model show
that this approach accelerates the minimisation in the first few iterations,
with better results when CVT performs well.


Abstract:
  We carry out a multi-probe self-consistency test of the flat $\Lambda$CDM
model with the aim of exploring potential causes of the reported tensions
between high- and low-redshift cosmological observations. We divide the model
into two theory regimes determined by the smooth background (geometry) and the
evolution of matter density fluctuations (growth), each governed by an
independent set of Lambda Cold Dark Matter ($\Lambda$CDM) cosmological
parameters. This extended model is constrained by a combination of weak
gravitational lensing measurements from the Kilo-Degree Survey, galaxy
clustering signatures extracted from Sloan Digital Sky Survey campaigns and the
Six-Degree Field Galaxy Survey, and the angular baryon acoustic scale and the
primordial scalar fluctuation power spectrum measured in $\textit{Planck}$
cosmic microwave background (CMB) data. We find strong consistency between the
geometry and growth parameters, and with the posterior of standard $\Lambda$CDM
analysis. Tension in the amplitude of matter density fluctuations as measured
by the parameter $S_8$ persists at around 3$\sigma$, with a $1.5\,\%$
constraint of $S_8 = 0.776_{-0.008}^{+0.016}$ for the combined probes. We also
observe a less significant preference (at least $2\sigma$) for higher values of
the Hubble constant, $H_0 = 70.5^{+0.7}_{-1.5}\,{\rm km\, s^{-1} Mpc^{-1}}$, as
well as for lower values of the total matter density parameter $\Omega_{\rm{m}}
= 0.289^{+0.007}_{-0.005}$ compared to the full $\textit{Planck}$ analysis.
Including the subset of the CMB information in the probe combination enhances
these differences rather than alleviate them, which we link to the discrepancy
between low and high multipoles in $\textit{Planck}$ data.


Abstract:
  In the previous four decades, a two-coil mutual inductance (MI) technique has
been widely employed in characterizing magnetic penetration depth, $\lambda$,
of superconducting films. However, the conventional methods used to obtain
$\lambda$ are not applicable to small-sized films with common shapes, which
limits the application of the MI technique in superconductivity research. Here,
we first employed the fast wavelet collocation (FWC) method to a two-coil
system and then proposed the possibility of directly obtaining the absolute
$\lambda$ of polygonal superconducting films with arbitrary sizes. To verify
its accuracy and advantages, we extracted the $\lambda$ values of square NbN
films with different sizes using the FWC and conventional flux leakage
subtraction (FLS) methods. Notably, the FLS method fails for a $5\times 5 \,
\rm mm^2$ film, which is attributed to the significant current peak at the film
edge. In contrast, the absolute $\lambda$ extracted using the FWC method was
independent of the film size. Finally, we established the applicability of the
FWC method to large coil spacings, which may pave the way for integrating
high-accuracy $\lambda$ measurements with the ionic liquid gating technique.


Abstract:
  Automated adversary emulation is becoming an indispensable tool of network
security operators in testing and evaluating their cyber defenses. At the same
time, it has exposed how quickly adversaries can propagate through the network.
While research has greatly progressed on quality decoy generation to fool human
adversaries, we may need different strategies to slow computer agents. In this
paper, we show that decoy generation can slow an automated agent's decision
process, but that the degree to which it is inhibited is greatly dependent on
the types of objects used. This points to the need to explicitly evaluate decoy
generation and placement strategies against fast moving, automated adversaries.


Abstract:
  A version of Connes Integration Formula which provides concrete asymptotics
of the eigenvalues is given. This radically extending the class of
quantum-integrable functions on compact Riemannian manifolds.


Abstract:
  Batteries have been identified as one most likely small UAS (sUAS) components
to fail in flight. sUAS safety will therefore be improved with redundant or
backup batteries. This paper presents a prognostics-informed Markov Decision
Process (MDP) model for managing multi-battery reconfiguration for sUAS
missions. Typical lithium polymer (Lipo) battery properties are experimentally
characterized and used in Monte Carlo simulations to establish battery dynamics
in sUAS flights of varying duration. Case studies illustrate the trade off
between multi-battery system increased complexity/weight and resilience to
non-ideal battery performance.


Abstract:
  In this work, we introduce a novel, end-to-end trainable CNN-based
architecture to deliver high quality results for grasp detection suitable for a
parallel-plate gripper, and semantic segmentation. Utilizing this, we propose a
novel refinement module that takes advantage of previously calculated grasp
detection and semantic segmentation and further increases grasp detection
accuracy. Our proposed network delivers state-of-the-art accuracy on two
popular grasp dataset, namely Cornell and Jacquard. As additional contribution,
we provide a novel dataset extension for the OCID dataset, making it possible
to evaluate grasp detection in highly challenging scenes. Using this dataset,
we show that semantic segmentation can additionally be used to assign grasp
candidates to object classes, which can be used to pick specific objects in the
scene.


Abstract:
  Increasing number in global COVID-19 cases demands for mathematical model to
analyze the interaction between the virus dynamics and the response of innate
and adaptive immunity. Here, based on the assumption of a weak and delayed
response of the innate and adaptive immunity in SARS-CoV-2 infection, we
constructed a mathematical model to describe the dynamic processes of immune
system. Integrating theoretical results with clinical COVID-19 patients' data,
we classified the COVID-19 development processes into three typical modes of
immune responses, correlated with the clinical classification of mild &
moderate, severe and critical patients. We found that the immune efficacy (the
ability of host to clear virus and kill infected cells) and the lymphocyte
supply (the abundance and pool of na\"ive T and B cell) play important roles in
the dynamic process and determine the clinical outcome, especially for the
severe and critical patients. Furthermore, we put forward possible treatment
strategies for the three typical modes of immune response. We hope our results
can help to understand the dynamical mechanism of the immune response against
SARS-CoV-2 infection, and to be useful for the treatment strategies and vaccine
design.


Abstract:
  We study the interplay between Mott physics, driven by Coulomb repulsion U,
and Hund physics, driven by Hund's coupling J, for a minimal model for Hund
metals, the orbital-symmetric three-band Hubbard-Hund model (3HHM) for a
lattice filling of 1/3. Hund-correlated metals are characterized by
spin-orbital separation (SOS), a Hund's-rule-induced two-stage Kondo-type
screening process, in which spin screening occurs at much lower energy scales
than orbital screening. By contrast, in Mott-correlated metals, lying close to
the phase boundary of a metal-insulator transition, the SOS window becomes
negligibly small and the Hubbard bands are well separated. Using dynamical
mean-field theory and the numerical renormalization group as real-frequency
impurity solver, we identify numerous fingerprints distinguishing Hundness from
Mottness in the temperature dependence of various physical quantities. These
include ARPES-type spectra, the local self-energy, static local orbital and
spin susceptibilities, resistivity, thermopower, and lattice and impurity
entropies. Our detailed description of the behavior of these quantities within
the context of a simple model Hamiltonian will be helpful for distinguishing
Hundness from Mottness in experimental and theoretical studies of real
materials.


Abstract:
  The classical Cram\'er-Lundberg risk process models the ruin probability of
an insurance company experiencing an incoming cash flow - the premium income,
and an outgoing cash flow - the claims. From a system's viewpoint, the web of
insurance agents and risk objects can be represented by a bipartite network. In
such a bipartite network setting, it has been shown that joint ruin of a group
of agents may be avoided even if individual agents would experience ruin in the
classical Cram\'er-Lundberg model. This paper describes and examines a phase
transition phenomenon for these ruin probabilities.


Abstract:
  In recent years, the use of lithium-ion batteries has greatly expanded into
products from many industrial sectors, e.g. cars, power tools or medical
devices. An early prediction and robust understanding of battery faults could
therefore greatly increase product quality in those fields. While current
approaches for data-driven fault prediction provide good results on the exact
processes they were trained on, they often lack the ability to flexibly adapt
to changes, e.g. in operational or environmental parameters. Continual learning
promises such flexibility, allowing for an automatic adaption of previously
learnt knowledge to new tasks. Therefore, this article discusses different
continual learning approaches from the group of regularization strategies,
which are implemented, evaluated and compared based on a real battery wear
dataset. Online elastic weight consolidation delivers the best results, but, as
with all examined approaches, its performance appears to be strongly dependent
on task characteristics and task sequence.


Abstract:
  Deployed into an open world, object detectors are prone to open-set errors,
false positive detections of object classes not present in the training
dataset. We propose GMM-Det, a real-time method for extracting epistemic
uncertainty from object detectors to identify and reject open-set errors.
GMM-Det trains the detector to produce a structured logit space that is
modelled with class-specific Gaussian Mixture Models. At test time, open-set
errors are identified by their low log-probability under all Gaussian Mixture
Models. We test two common detector architectures, Faster R-CNN and RetinaNet,
across three varied datasets spanning robotics and computer vision. Our results
show that GMM-Det consistently outperforms existing uncertainty techniques for
identifying and rejecting open-set detections, especially at the low-error-rate
operating point required for safety-critical applications. GMM-Det maintains
object detection performance, and introduces only minimal computational
overhead. We also introduce a methodology for converting existing object
detection datasets into specific open-set datasets to evaluate open-set
performance in object detection.


Abstract:
  The hair and beauty industry is a fast-growing industry. This led to the
development of various applications, such as virtual hair dyeing or hairstyle
transfer, to satisfy the customer's needs. Although several hairstyle datasets
are available for these applications, they often consist of a relatively small
number of images with low resolution, thus limiting their performance on
high-quality hair editing. In response, we introduce a novel large-scale Korean
hairstyle dataset, K-hairstyle, containing 500,000 high-resolution images. In
addition, K-hairstyle includes various hair attributes annotated by Korean
expert hairstylists as well as hair segmentation masks. We validate the
effectiveness of our dataset via several applications, such as hair dyeing,
hairstyle transfer, and hairstyle classification. K-hairstyle is publicly
available at https://psh01087.github.io/K-Hairstyle/.


Abstract:
  Understanding the role of time-varying pollution mixtures on human health is
critical as people are simultaneously exposed to multiple pollutants during
their lives. For vulnerable sub-populations who have well-defined exposure
periods (e.g., pregnant women), questions regarding critical windows of
exposure to these mixtures are important for mitigating harm. We extend
Critical Window Variable Selection (CWVS) to the multipollutant setting by
introducing CWVS for Mixtures (CWVSmix), a hierarchical Bayesian method that
combines smoothed variable selection and temporally correlated weight
parameters to (i) identify critical windows of exposure to mixtures of
time-varying pollutants, (ii) estimate the time-varying relative importance of
each individual pollutant and their first order interactions within the
mixture, and (iii) quantify the impact of the mixtures on health. Through
simulation, we show that CWVSmix offers the best balance of performance in each
of these categories in comparison to competing methods. Using these approaches,
we investigate the impact of exposure to multiple ambient air pollutants on the
risk of stillbirth in New Jersey, 2005-2014. We find consistent elevated risk
in gestational weeks 2, 16-17, and 20 for non-Hispanic Black mothers, with
pollution mixtures dominated by ammonium (weeks 2, 17, 20), nitrate (weeks 2,
17), nitrogen oxides (weeks 2, 16), PM2.5 (week 2), and sulfate (week 20). The
method is available in the R package CWVSmix.


Abstract:
  In this letter, we propose a blockwise phase rotation-aided analog transmit
beamforming (BPR-ATB) scheme to improve the spectral efficiency and the
bit-error-rate (BER) performance in millimeter wave (mmWave) communication
systems. Due to the phase angle optimization issues of the conventional analog
beamforming, we design the BPR-ATB for reducing the rotated beamspace of the
equivalent channel and improving the minimum Euclidean distance. To verify the
effectiveness of the proposed BPR-ATB scheme, we employ an Alamouti coding
technique at the transmitter and evaluate the bit-error-rate performance for
mmWave multiple-input and single-output systems. The simulation results show
that the proposed BPR-ATB scheme outperforms the conventional discrete Fourier
transform-based ATB scheme.


Abstract:
  We identify the large-$N$ scaling of the metrological quantum gain offered by
over-squeezed spin states that are accessible by one-axis-twisting, as a
function of the preparation time. We further determine how the scaling is
modified by relevant decoherence processes and predict a discontinuous change
of the quantum gain at a critical preparation time that depends on the noise.
Our analytical results provide recipes for optimal and feasible implementations
of quantum enhancements with non-Gaussian spin states in existing experiments,
well beyond the reach of spin squeezing.


Abstract:
  Graph self-supervised learning has gained increasing attention due to its
capacity to learn expressive node representations. Many pretext tasks, or loss
functions have been designed from distinct perspectives. However, we observe
that different pretext tasks affect downstream tasks differently cross
datasets, which suggests that searching pretext tasks is crucial for graph
self-supervised learning. Different from existing works focusing on designing
single pretext tasks, this work aims to investigate how to automatically
leverage multiple pretext tasks effectively. Nevertheless, evaluating
representations derived from multiple pretext tasks without direct access to
ground truth labels makes this problem challenging. To address this obstacle,
we make use of a key principle of many real-world graphs, i.e., homophily, or
the principle that ``like attracts like,'' as the guidance to effectively
search various self-supervised pretext tasks. We provide theoretical
understanding and empirical evidence to justify the flexibility of homophily in
this search task. Then we propose the AutoSSL framework which can automatically
search over combinations of various self-supervised tasks. By evaluating the
framework on 7 real-world datasets, our experimental results show that AutoSSL
can significantly boost the performance on downstream tasks including node
clustering and node classification compared with training under individual
tasks. Code will be released at https://github.com/ChandlerBang/AutoSSL.


Abstract:
  We investigate a new algebraic structure which always gives rise to a
set-theoretic solution of the Yang-Baxter equation. Specifically, a skew (left)
inverse semi-brace is a non-empty set $S$ endowed with two binary operations
$+$ and $\circ$ such that both $(S,+)$ and $(S, \circ)$ are inverse semigroups
and they hold
  \begin{align*}
  a \circ \left(b+c\right) = a\circ b - a +a\circ c \qquad \text{and} \qquad
  a\circ a^- = - a + a,
  \end{align*}
  for all $a,b,c \in S$, where $-a$ and $a^-$ are the inverses of $a$ with
respect to $+$ and $\circ$, respectively. In particular, such structures
include that of skew braces and form a subclass of inverse semi-braces. Any
solution $r$ associated to an arbitrary skew inverse semi-brace $S$ has a
behavior close to bijectivity, namely $r$ is a completely regular element in
the full transformation semigroup on $S\times S$. In addition, we provide some
methods to construct skew inverse semi-braces.


Abstract:
  We study several fairness notions in allocating indivisible chores (i.e.,
items with non-positive values) to agents who have additive and submodular cost
functions. The fairness criteria we are concern with are envy-free up to any
item (EFX), envy-free up to one item (EF1), maximin share (MMS), and pairwise
maximin share (PMMS), which are proposed as relaxations of envy-freeness in the
setting of additive cost functions. For allocations under each fairness
criterion, we establish their approximation guarantee for other fairness
criteria. Under the additive setting, our results show strong connections
between these fairness criteria and, at the same time, reveal intrinsic
differences between goods allocation and chores allocation. However, such
strong relationships cannot be inherited by the submodular setting, under which
PMMS and MMS are no longer relaxations of envy-freeness and, even worse, few
non-trivial guarantees exist. We also investigate efficiency loss under these
fairness constraints and establish their prices of fairness.


Abstract:
  It is not hard to see that the need for clean water is growing by considering
the decrease of the water sources day by day in the world. Potable fresh water
is also used for irrigation, so it should be planned to decrease freshwater
wastage. With the development of technology and the availability of cheaper and
more effective solutions, the efficiency of irrigation increased and the water
loss can be reduced. In particular, Internet of things (IoT) devices has begun
to be used in all areas. We can easily and precisely collect temperature,
humidity and mineral values from the irrigation field with the IoT devices and
sensors. Most of the operations and decisions about irrigation are carried out
by people. For people, it is hard to have all the real-time data such as
temperature, moisture and mineral levels in the decision-making process and
make decisions by considering them. People usually make decisions with their
experience. In this study, a wide range of information from the irrigation
field was obtained by using IoT devices and sensors. Data collected from IoT
devices and sensors sent via communication channels and stored on MongoDB. With
the help of Weka software, the data was normalized and the normalized data was
used as a learning set. As a result of the examinations, a decision tree (J48)
algorithm with the highest accuracy was chosen and an artificial intelligence
model was created. Decisions are used to manage operations such as starting,
maintaining and stopping the irrigation. The accuracy of the decisions was
evaluated and the irrigation system was tested with the results. There are
options to manage, view the system remotely and manually and also see the
system s decisions with the created mobile application.


Abstract:
  Randomized Smoothing (RS) is a promising method for obtaining robustness
certificates by evaluating a base model under noise. In this work, we: (i)
theoretically motivate why ensembles are a particularly suitable choice as base
models for RS, and (ii) empirically confirm this choice, obtaining
state-of-the-art results in multiple settings. The key insight of our work is
that the reduced variance of ensembles over the perturbations introduced in RS
leads to significantly more consistent classifications for a given input. This,
in turn, leads to substantially increased certifiable radii for samples close
to the decision boundary. Additionally, we introduce key optimizations which
enable an up to 55-fold decrease in sample complexity of RS, thus drastically
reducing its computational overhead. Experimentally, we show that ensembles of
only 3 to 10 classifiers consistently improve on their strongest constituting
model with respect to their average certified radius (ACR) by 5% to 21% on both
CIFAR10 and ImageNet, achieving a new state-of-the-art ACR of 0.86 and 1.11,
respectively. We release all code and models required to reproduce our results
upon publication.


Abstract:
  The possibility of suddenly ionized molecules undergoing extremely fast
electron hole dynamics prior to significant structural change was first
recognized more than 20 years ago and termed charge migration. The accurate
probing of ultrafast electron hole dynamics requires measurements that have
both sufficient temporal resolution and can detect the localization of a
specific hole within the molecule. We report an investigation of the dynamics
of inner valence hole states in isopropanol where we use an x-ray pump/x-ray
probe experiment, with site and state-specific probing of a transient hole
state localized near the oxygen atom in the molecule, together with an ab
initio theoretical treatment. We record the signature of transient hole
dynamics and make the first observation of dynamics driven by frustrated
Auger-Meitner transitions. We verify that the hole lifetime is consistent with
our theoretical prediction. This state-specific measurement paves the way to
widespread application for observations of transient hole dynamics localized in
space and time in molecules and thus to charge transfer phenomena that are
fundamental in chemical and material physics.


Abstract:
  A significant fraction of baryons in galaxies are in the form of diffuse gas
of the circumgalactic medium (CGM). One critical component of the multi-phases
of CGM, the so-called "coronal" warm-hot phase gas ($\rm 10^{5}-10^{6}$ K)
traced by O VI 1031.93, 1037.62 \r{A} resonance lines, has rarely been detected
in emission from galaxy halos other than Milky Way. Here we report four
additional detections of O VI emission gas in the halos of nearby edge-on
galaxies, NGC 4631 and NGC 891, using archival Far Ultraviolet Spectroscopic
Explorer data and an updated data pipeline. We find the most intense O VI
emission to be from fields forming a vertical line near the center of NGC 4631,
despite the close proximity to the disk of two other fields. The detected O VI
emission surface brightness are about 1.1$\pm 0.3$ $\times$ $10^{-18}$ to
3.9$\pm0.8$ $\times$ $10^{-18}$ ergs s$^{-1}$ cm$^{-2}$ arcsec$^{-2}$. The
spatial distribution of the five 30" $\times$ 30" O VI detection fields in NGC
4631 can be interpreted as the existence of filamentary structures of more
intense O VI emission superimposed within a diffuse and faint O VI halo in
star-forming galaxies. Volume-filled O VI emission mapping is greatly needed to
determine the structure and prevalence of warm-hot gas and the role it plays in
the cycling of gas between the galaxy disk and the halo. Finally, we present
the sensitivity of future funded and proposed UV missions (LUVOIR-A, LUVOIR-B,
CETUS, and Aspera) to the detection of diffuse and faint O VI emission in
nearby galaxy halos.


Abstract:
  Data augmentation is commonly used to help build a robust speaker
verification system, especially in limited-resource case. However, conventional
data augmentation methods usually focus on the diversity of acoustic
environment, leaving the lexicon variation neglected. For text dependent
speaker verification tasks, it's well-known that preparing training data with
the target transcript is the most effectual approach to build a well-performing
system, however collecting such data is time-consuming and expensive. In this
work, we propose a unit selection synthesis based data augmentation method to
leverage the abundant text-independent data resources. In this approach
text-independent speeches of each speaker are firstly broke up to speech
segments each contains one phone unit. Then segments that contain phonetics in
the target transcript are selected to produce a speech with the target
transcript by concatenating them in turn. Experiments are carried out on the
AISHELL Speaker Verification Challenge 2019 database, the results and analysis
shows that our proposed method can boost the system performance significantly.


Abstract:
  Low-light image enhancement (LLIE) aims at improving the perception or
interpretability of an image captured in an environment with poor illumination.
Recent advances in this area are dominated by deep learning-based solutions,
where many learning strategies, network structures, loss functions, training
data, etc. have been employed. In this paper, we provide a comprehensive survey
to cover various aspects ranging from algorithm taxonomy to open issues. To
examine the generalization of existing methods, we propose a low-light image
and video dataset, in which the images and videos are taken by different mobile
phones' cameras under diverse illumination conditions. Besides, for the first
time, we provide a unified online platform that covers many popular LLIE
methods, of which the results can be produced through a user-friendly web
interface. In addition to qualitative and quantitative evaluation of existing
methods on publicly available and our proposed datasets, we also validate their
performance in face detection in the dark.This survey together with the
proposed dataset and online platform could serve as a reference source for
future study and promote the development of this research field. The proposed
platform and dataset as well as the collected methods, datasets, and evaluation
metrics are publicly available and will be regularly updated.


Abstract:
  The efficiency of the transport of angular momentum and chemical elements
inside intermediate-mass stars lacks proper calibration, thereby introducing
uncertainties on a star's evolutionary pathway. Improvements require better
estimation of stellar masses, evolutionary stages, and internal mixing
properties. We aim to develop a neural network approach for asteroseismic
modelling and test its capacity to provide stellar masses, ages, and
overshooting parameter for a sample of 37 $\gamma$ Doradus stars. Here, our
goal is to perform the parameter estimation from modelling of individual
periods measured for dipole modes with consecutive radial order. We have
trained neural networks to predict theoretical pulsation periods of high-order
gravity modes as well as the luminosity, effective temperature, and surface
gravity for a given mass, age, overshooting parameter, diffusive envelope
mixing, metallicity, and near-core rotation frequency. We have applied our
neural networks for Computing Pulsation Periods and Photospheric Observables,
C-3PO, to our sample and compute grids of stellar pulsation models for the
estimated parameters. We present the near-core rotation rates (from literature)
as a function of the inferred stellar age and critical rotation rate. We assess
the rotation rates of the sample near the start of the main sequence assuming
rigid rotation. Furthermore, we measure the extent of the core overshoot region
and find no correlation with mass, age, or rotation. The neural network
approach developed in this study allows for the derivation of stellar
properties dominant for stellar evolution -- such as mass, age, and extent of
core-boundary mixing. It also opens a path for future estimation of mixing
profiles throughout the radiative envelope, with the aim to infer those
profiles for large samples of $\gamma$ Doradus stars.


Abstract:
  Security is one of the main issues in Internet of Things (IoT). Encryption
plays a curtail role in making these systems secure. Substitution Box (S-Box)
has an effective impact in block encryption methods. Due to the restricted
resource capacities of IoT nodes, providing a lightweight S-Box is a
challenging problem. This paper presents a key-dependent S-Box using
Hyperelliptic curve. The proposed S-Box is analytically evaluated using
performance criteria including bijection, nonlinearity, strict avalanche
effect, and algebraic degree. The evaluation results endorse that the offered
S-Box production algorithm is considerably an effective way to generate
cryptographic strong S-Box.


Abstract:
  We establish Gagliardo-Nirenberg-Sobolev type inequalities on nonlocal
Sobolev spaces driven by $p$-L\'evy integrable functions, by imposing some
appropriate growth conditions on the associated critical function. This gives
rise to the embedding of the local and the nonlocal Sobolev spaces into Orlicz
type spaces. The Gagliardo-Nirenberg-Sobolev type inequalities, as in the
classical context, turn out to have some reciprocity with Poincar\'e and
Poincar\'e-Sobolev type inequalities. The classical fractional Sobolev
inequality is also derived as a direct consequence.


Abstract:
  We revisit the dependence of covering factor (CF) of dust torus on physical
properties of active galactic nuclei (AGNs) by taking into account an AGN polar
dust emission. The CF is converted from a ratio of infrared (IR) luminosity
contributed from AGN dust torus ($L_{\rm IR}^{\rm torus}$) and AGN bolometric
luminosity ($L_{\rm bol}$), by assuming a non-linear relation between
luminosity ratio and intrinsic CF. We select 37,181 type 1 quasars at $z < 0.7$
from the Sloan Digital Sky Survey Data Release 16 quasar catalog. Their $L_{\rm
bol}$, black hole mass ($M_{\rm BH}$), and Eddington ratio ($\lambda_{\rm
Edd}$) are derived by spectral fitting with QSFit. We conduct spectral energy
distribution decomposition by using X-CIGALE with clumpy torus and polar dust
model to estimate $L_{\rm IR}^{\rm torus}$ without being affected by the
contribution of stellar and AGN polar dust to IR emission. For 5720 quasars
whose physical quantities are securely determined, we perform a correlation
analysis on CF and (i) $L_{\rm bol}$, (ii) $M_{\rm BH}$, and (iii)
$\lambda_{\rm Edd}$. As a result, anti-correlations for CF-$L_{\rm bol}$,
CF-$M_{\rm BH}$, and CF-$\lambda_{\rm Edd}$ are confirmed. We find that
incorporating the AGN polar dust emission makes those anti-correlations
stronger which are compared to those without considering it. This indicates
that polar dust wind provably driven by AGN radiative pressure is one of the
key components to regulate obscuring material of AGNs.


Abstract:
  The last decade has seen a significant increase of interest in deep learning
research, with many public successes that have demonstrated its potential. As
such, these systems are now being incorporated into commercial products. With
this comes an additional challenge: how can we build AI systems that solve
tasks where there is not a crisp, well-defined specification? While multiple
solutions have been proposed, in this competition we focus on one in
particular: learning from human feedback. Rather than training AI systems using
a predefined reward function or using a labeled dataset with a predefined set
of categories, we instead train the AI system using a learning signal derived
from some form of human feedback, which can evolve over time as the
understanding of the task changes, or as the capabilities of the AI system
improve.
  The MineRL BASALT competition aims to spur forward research on this important
class of techniques. We design a suite of four tasks in Minecraft for which we
expect it will be hard to write down hardcoded reward functions. These tasks
are defined by a paragraph of natural language: for example, "create a
waterfall and take a scenic picture of it", with additional clarifying details.
Participants must train a separate agent for each task, using any method they
want. Agents are then evaluated by humans who have read the task description.
To help participants get started, we provide a dataset of human demonstrations
on each of the four tasks, as well as an imitation learning baseline that
leverages these demonstrations.
  Our hope is that this competition will improve our ability to build AI
systems that do what their designers intend them to do, even when the intent
cannot be easily formalized. Besides allowing AI to solve more tasks, this can
also enable more effective regulation of AI systems, as well as making progress
on the value alignment problem.


Abstract:
  In the Landau levels of a two-dimensional electron system or when flat bands
are present, e.g. in twisted van der Waals bilayers, strong electron-electron
interaction gives rise to quantum Hall ferromagnetism with spontaneously broken
symmetries in the spin and isospin sectors. Quantum Hall ferromagnets support a
rich variety of low-energy collective excitations that are instrumental to
understand the nature of the magnetic ground states and are also potentially
useful as carriers of quantum information. Probing such collective excitations,
especially their dispersion {\omega}(k), has been experimentally challenging
due to small sample size and measurement constraints. In this work, we
demonstrate an all-electrical approach that integrates a Fabry-P\'erot cavity
with non-equilibrium transport to achieve the excitation, wave vector selection
and detection of spin waves in graphene heterostructures. Our experiments
reveal gapless, linearly dispersed spin wave excitations in the E = 0 Landau
level of bilayer graphene, thus providing direct experimental evidence for a
predicted canted antiferromagnetic order. We show that the gapless spin wave
mode propagates with a high group velocity of several tens of km/s and
maintains phase coherence over a distance of many micrometers. Its dependence
on the magnetic field and temperature agree well with the hydrodynamic theory
of spin waves. These results lay the foundation for the quest of spin
superfluidity in this high-quality material. The resonant cavity technique we
developed offers a powerful and timely method to explore the collective
excitation of many spin and isospin-ordered many-body ground states in van der
Waals heterostructures and open the possibility of engineering magnonic
devices.


Abstract:
  In this note we introduce a notion of free cofibrations of permutative
categories. We show that each cofibration of permutative categories is a
retract of a free cofibration.


Abstract:
  Until the beginning of 2021, lung cancer is known to be the most common
cancer in the world. The disease is common due to factors such as occupational
exposure, smoking and environmental pollution. The early diagnosis and
treatment of the disease is of great importance as well as the prevention of
the causes that cause the disease. The study was planned to create a web
interface that works with machine learning algorithms to predict prognosis
using lung cancer clinical and gene expression in the GDC data portal.


Abstract:
  This article describes techniques employed in the production of a synthetic
dataset of driver telematics emulated from a similar real insurance dataset.
The synthetic dataset generated has 100,000 policies that included observations
about driver's claims experience together with associated classical risk
variables and telematics-related variables. This work is aimed to produce a
resource that can be used to advance models to assess risks for usage-based
insurance. It follows a three-stage process using machine learning algorithms.
The first stage is simulating values for the number of claims as multiple
binary classifications applying feedforward neural networks. The second stage
is simulating values for aggregated amount of claims as regression using
feedforward neural networks, with number of claims included in the set of
feature variables. In the final stage, a synthetic portfolio of the space of
feature variables is generated applying an extended $\texttt{SMOTE}$ algorithm.
The resulting dataset is evaluated by comparing the synthetic and real datasets
when Poisson and gamma regression models are fitted to the respective data.
Other visualization and data summarization produce remarkable similar
statistics between the two datasets. We hope that researchers interested in
obtaining telematics datasets to calibrate models or learning algorithms will
find our work valuable.


Abstract:
  We consider graphical $n$-person games with perfect information that have no
Nash equilibria in pure stationary strategies. Solving these games in mixed
strategies, we introduce probabilistic distributions in all non-terminal
positions. The corresponding plays can be analyzed under two different basic
assumptions: Markov's and a priori realizations. The former one guarantees
existence of a uniformly best response of each player in every situation.
Nevertheless, Nash equilibrium may fail to exist even in mixed strategies. The
classical Nash theorem is not applicable, since Markov's realizations may
result in the limit distributions and effective payoff functions that are not
continuous. The a priori realization does not share many nice properties of the
Markov one (for example, existence of the uniformly best response) but in
return, Nash's theorem is applicable. We illustrate both realizations in
details by two examples with $2$ and $3$ players and also provide some general
results.


Abstract:
  In this work, we introduce a novel data-driven model-reference control design
approach for unknown linear systems with fully measurable state. The proposed
control action is composed by a static feedback term and a reference tracking
block, which are shaped from data to reproduce the desired behavior in
closed-loop. By focusing on the case where the reference model and the plant
share the same order, we propose an optimal design procedure with Lyapunov
stability guarantees, tailored to handle state measurements with additive
noise. Two simulation examples are finally illustrated to show the potential of
the proposed strategy as compared to the state of the art approaches.


Abstract:
  Lattice-based cryptography is one of the leading proposals for post-quantum
cryptography. The Shortest Vector Problem (SVP) is arguably the most important
problem for the cryptanalysis of lattice-based cryptography, and many
lattice-based schemes have security claims based on its hardness. The best
quantum algorithm for the SVP is due to Laarhoven [Laa16 PhD] and runs in
(heuristic) time $2^{0.2653d + o(d)}$. In this article, we present an
improvement over Laarhoven's result and present an algorithm that has a
(heuristic) running time of $2^{0.2570 d + o(d)}$ where $d$ is the lattice
dimension. We also present time-memory trade-offs where we quantify the amount
of quantum memory and quantum random access memory of our algorithm. The core
idea is to replace Grover's algorithm used in [Laa16 PhD] in a key part of the
sieving algorithm by a quantum random walk in which we add a layer of local
sensitive filtering.


Abstract:
  This paper proposes a method for OOD detection. Questioning the premise of
previous studies that ID and OOD samples are separated distinctly, we consider
samples lying in the intermediate of the two and use them for training a
network. We generate such samples using multiple image transformations that
corrupt inputs in various ways and with different severity levels. We estimate
where the generated samples by a single image transformation lie between ID and
OOD using a network trained on clean ID samples. To be specific, we make the
network classify the generated samples and calculate their mean classification
accuracy, using which we create a soft target label for them. We train the same
network from scratch using the original ID samples and the generated samples
with the soft labels created for them. We detect OOD samples by thresholding
the entropy of the predicted softmax probability. The experimental results show
that our method outperforms the previous state-of-the-art in the standard
benchmark tests. We also analyze the effect of the number and particular
combinations of image corrupting transformations on the performance.


Abstract:
  The angular-dependent magnetoresistance (AMR) of the ab plane is measured on
the single crystals of FeSe1-xSx (x = 0, 0.07, 0.13 and 1) and FeSe1-yTey (y =
0.06, 0.61 and 1) at various temperatures under fields up to 9 T. A pronounced
twofold-anisotropic carrier-scattering effect is identified by AMR, and
attributed to a magnetic-field-induced spin nematicity that emerges from the
tetragonal normal-state regime below a characteristic temperature Tsn. This
magnetically polarized spin nematicity is found to be ubiquitous in the
isoelectronic FeSe1-xSx and FeSe1-yTey systems, no matter whether the sample
shows an electronic nematic order at Ts < Tsn, or an antiferromagnetic order at
TN < Tsn, or neither order. Importantly, we find that the isoelectronic
substitution with sulfur does not suppress but even enhances the characteristic
Tsn of the induced spin nematicity in FeSe1-xSx samples. This contrasts sharply
with their rapidly suppressed Ts, the transition temperature of the spontaneous
electronic nematicity. Furthermore, we find that the superconductivity is
significantly suppressed with the enhancement of the induced spin nematicity in
both FeSe1-xSx and FeSe1-yTey samples.


Abstract:
  Huge computational costs brought by convolution and batch normalization (BN)
have caused great challenges for the online training and corresponding
applications of deep neural networks (DNNs), especially in resource-limited
devices. Existing works only focus on the convolution or BN acceleration and no
solution can alleviate both problems with satisfactory performance. Online
training has gradually become a trend in resource-limited devices like mobile
phones while there is still no complete technical scheme with acceptable model
performance, processing speed, and computational cost. In this research, an
efficient online-training quantization framework termed EOQ is proposed by
combining Fixup initialization and a novel quantization scheme for DNN model
compression and acceleration. Based on the proposed framework, we have
successfully realized full 8-bit integer network training and removed BN in
large-scale DNNs. Especially, weight updates are quantized to 8-bit integers
for the first time. Theoretical analyses of EOQ utilizing Fixup initialization
for removing BN have been further given using a novel Block Dynamical Isometry
theory with weaker assumptions. Benefiting from rational quantization
strategies and the absence of BN, the full 8-bit networks based on EOQ can
achieve state-of-the-art accuracy and immense advantages in computational cost
and processing speed. What is more, the design of deep learning chips can be
profoundly simplified for the absence of unfriendly square root operations in
BN. Beyond this, EOQ has been evidenced to be more advantageous in small-batch
online training with fewer batch samples. In summary, the EOQ framework is
specially designed for reducing the high cost of convolution and BN in network
training, demonstrating a broad application prospect of online training in
resource-limited devices.


Abstract:
  Machine learning (ML) has been well applied to studying equilibrium phase
transition models, by accurately predicating critical thresholds and some
critical exponents. Difficulty will be raised, however, for integrating ML into
non-equilibrium phase transitions. The extra dimension in a given
non-equilibrium system, namely time, can greatly slow down the procedure
towards the steady state. In this paper we find that by using some simple
techniques of ML, non-steady state configurations of directed percolation (DP)
suffice to capture its essential critical behaviors in both (1+1) and (2+1)
dimensions. With the supervised learning method, the framework of our binary
classification neural networks can identify the phase transition threshold, as
well as the spatial and temporal correlation exponents. The characteristic time
$t_{c}$, specifying the transition from active phases to absorbing ones, is
also a major product of the learning. Moreover, we employ the convolutional
autoencoder, an unsupervised learning technique, to extract dimensionality
reduction representations and cluster configurations of (1+1) bond DP. It is
quite appealing that such a method can yield a reasonable estimation of the
critical point.


Abstract:
  We propose an inferential approach for maximum likelihood estimation of the
hidden Markov models for continuous responses. We extend to the case of
longitudinal observations the finite mixture model of multivariate Gaussian
distributions with Missing At Random (MAR) outcomes, also accounting for
possible dropout. The resulting hidden Markov model accounts for different
types of missing pattern: (i) partially missing outcomes at a given time
occasion; (ii) completely missing outcomes at a given time occasion
(intermittent pattern); (iii) dropout before the end of the period of
observation (monotone pattern). The MAR assumption is formulated to deal with
the first two types of missingness, while to account for informative dropout we
assume an extra absorbing state. Maximum likelihood estimation of the model
parameters is based on an extended Expectation-Maximization algorithm relying
on suitable recursions. The proposal is illustrated by a Monte Carlo simulation
study and an application based on historical data on primary biliary
cholangitis.


Abstract:
  We design, in a most simple way, Fabry-Perot cavities with longitudinal
chiral modes by sandwiching between two smooth metallic silver mirrors a layer
of polystyrene made planar chiral by torsional shear stress. We demonstrate
that the helicity-preserving features of our cavities stem from a spin-orbit
coupling mechanism seeded inside the cavities by the specific chiroptical
features of planar chirality. Planar chirality gives rise to an extrinsic
source of three-dimensional chirality under oblique illumination that endows
the cavities with enantiomorphic signatures measured experimentally and
simulated with excellent agreement. The simplicity of our scheme is
particularly promising in the context of chiral cavity QED and polaritonic
asymmetric chemistry.


Abstract:
  Composite quantum compounds (CQCs) have become an important avenue for the
investigation of inter-correlation between two distinct phenomenon in physics.
Topological superconductors, axion insulators etc. are few such CQCs which have
recently drawn tremendous attention in the community. Topological nontriviality
and Rashba spin physics are two different quantum phenomena but can be
intertwined within a CQC platform. In this letter, we present a general
symmetry based mechanism, supported by \textit{ab-initio} calculations to
achieve intertwined giant Rashba splitting and topological non-trivial states
simultaneously in a single crystalline system. Such co-existent properties can
further be tuned to achieve other rich phenomenon. We have achieved Rashba
splitting energy ($\Delta E$) and Rashba coefficient ($\alpha$) values as large
as 161 meV and 4.87 eV$\r{A}$ respectively in conjunction with Weyl semimetal
phase in KSnSb$_{0.625}$Bi$_{0.375}$. Interestingly, these values are even
larger than the values reported for widely studied topologically trivial Rashba
semiconductor BiTeI. The advantage of our present analysis is that one can
achieve various topological phases without compromising the Rashba parameters,
within this CQC platform.


Abstract:
  In this short paper we report on student programming competition results by
students from the Computer Science Department (COSC) of Okanagan College (OC)
and discuss the achieved results from an educational point of view. We found
that some freshmen and sophomore students in diploma and degree programs are
very capable and eager to be involved in applied research projects as early as
the second semester, and into local and international programming competitions
as well. Our observation is based on the last 2 educational years, beginning
2015 when we introduced programming competitions to COSC students. Students
reported that participation in competitions give them motivation to effectively
learn in their programming courses, inspire them to learn deeper and more
thoroughly, and help them achieve better results in their classes.


Abstract:
  To derive the properties of the dense cores in the galactic star-forming
complex NGC6357 and to investigate the effects of an intense far-UV radiation
field on their properties, we mapped the region at 450 and 850 micron, and in
the CO(3-2) line with the JCMT. We also made use of the Herschel Hi-GAL data at
70 and 160 micron. We used Gaussclumps to retrieve 686 compact cores embedded
in the diffuse sub-mm emission and constructed their SED from 70 to 850 micron,
from which we derived mass and temperature. The estimated mass completeness
limit is ~5Mo. We divided the observed area in an 'active' region, exposed to
the far-UV radiation from the more massive members of three star clusters (411
cores), and a 'quiescent' region, less affected by far-UV radiation (275
cores). We also attempted to select a sample of pre-stellar cores based on
cross-correlation with 70 micron emission and red WISE point sources. Most of
the cores above the mass completeness limit are likely to be gravitationally
bound. The fraction of gas in dense cores is very low, 1.4%. We found a
mass-size relation log(M/Mo) ~ (2.0-2.4) x log (D/arcsec), depending on the
precise selection of the sample. The temperature distributions in the two
sub-regions are clearly different, peaking at ~25K in the quiescent region and
at ~35K in the active region. The core mass functions are different as well, at
a 2sigma level, consistent with a Salpeter IMF in the quiescent region and
flatter than that in the active region. The dense cores lying close to the HII
regions are consistent with pre-existing cores being gradually engulfed by a
PDR and photoevaporating. We attribute the different global properties of dense
cores in the two sub-regions to the influence of the far-UV radiation field.


Abstract:
  Monitoring complex systems results in massive multivariate time series data,
and anomaly detection of these data is very important to maintain the normal
operation of the systems. Despite the recent emergence of a large number of
anomaly detection algorithms for multivariate time series, most of them ignore
the correlation modeling among multivariate, which can often lead to poor
anomaly detection results. In this work, we propose a novel anomaly detection
model for multivariate time series with \underline{HI}gh-order
\underline{F}eature \underline{I}nteractions (HIFI). More specifically, HIFI
builds multivariate feature interaction graph automatically and uses the graph
convolutional neural network to achieve high-order feature interactions, in
which the long-term temporal dependencies are modeled by attention mechanisms
and a variational encoding technique is utilized to improve the model
performance and robustness. Extensive experiments on three publicly available
datasets demonstrate the superiority of our framework compared with
state-of-the-art approaches.


Abstract:
  Stratifying factors, like age and gender, can modify the effect of treatments
and exposures on risk of a studied outcome. Several effect measures, including
the relative risk, hazard ratio, odds ratio, and risk difference, can be used
to measure this modification. It is known that choice of effect measure may
determine the presence and direction of effect-measure modification. We show
that considering the opposite outcome -- for example, recovery instead of death
-- may similarly influence effect-measure modification. In fact, if the
relative risk for the studied outcome and the relative risk for the opposite
outcome agree about the direction of effect-measure modification, then so will
the two cumulative hazard ratios, the risk difference, and the odds ratio. When
risks are randomly sampled from the uniform (0,1) distribution, the probability
of this happening is 5/6. Disagreement is probable enough that researchers
considering one relative risk should also consider the other and further
discussion if they disagree. (If possible, researchers should also report
estimated risks.) We provide examples through case studies on HCV, COVID-19,
and bankruptcy following melanoma treatment.


Abstract:
  Binary population synthesis (BPS) employs prescriptions to predict final
fates, explosion or implosion, and remnant masses based on one or two stellar
parameters at the evolutionary cutoff imposed by the code, usually at or near
central carbon ignition. In doing this, BPS disregards the integral role
late-stage evolution plays in determining the final fate, remnant type, and
remnant mass within the neutrino-driven explosion paradigm. To highlight
differences between a popular prescription which relies only on the core and
final stellar mass and emerging methods which rely on a star's presupernova
core structure, we generate a series of compact object distributions using
three different methods for a sample population of single and binary stars
computed in BPASS. The first method estimates remnant mass based on a star's
carbon-oxygen (CO) core mass and final total mass. The second method uses the
presupernova core structure based on the bare CO-core models of \citet{Pat20}
combined with a parameterized explosion criterion to first determine final fate
and remnant type, then remnant mass. The third method associates presupernova
helium-core masses with remnant masses determined from public explosion models
which rely implicitly on core structure. We find that the core-/final
mass-based prescription favors lower mass remnants, including a large
population of mass gap black holes, and predicts neutron star masses which span
a wide range, whereas the structure-based prescriptions favor slightly higher
mass remnants, mass gap black holes only as low as 3.5 \Msun, and predict
neutron star mass distributions which cluster in a narrow range.


Abstract:
  We estimate the solar peculiar velocities and Oort constants using a sample
of 5,627 A-type stars with $d<0.6\,\rm kpc$ and $|z|<0.1\,\rm kpc$, selected
from the LAMOST surveys. The radial and tangential velocities of these A-type
stars are fitted by using a non-axisymmetric model. The best-fitting result
yields the solar peculiar velocities $(U_\odot,V_\odot,W_\odot)=(11.69\pm0.68,
10.16\pm0.51, 7.67\pm0.10)\,\rm km\,s^{-1}$ and Oort constants
$A=16.31\pm0.89\,\rm km\,s^{-1}\,kpc^{-1}$, $B=-11.99\pm0.79\,\rm
km\,s^{-1}\,kpc^{-1}$, $C=-3.10\pm0.48\,\rm km\,s^{-1}\,kpc^{-1}$,
$K=-1.25\pm1.04\,\rm km\,s^{-1}\,kpc^{-1}$, respectively. $|K+C|>4\,\rm
km\,s^{-1}\,kpc^{-1}$ means that there is a radial velocity gradient in the
extended local disk, implying the local disk is in a non-asymmetric potential.
Using the derived Oort constants, we derive the local angular velocity
$\Omega\,{\approx}\,A-B=28.30\pm1.19\,\rm km\,s^{-1}\,kpc^{-1}$. By using
A-type star sample of different volumes, we further try to evaluate the impacts
of the ridge pattern in $R$-$V_{\phi}$ plane on constraining the solar motions
and Oort constants. As the volume becomes larger toward the anti-center
direction, the values of $A$ and $B$ become larger (implying a steeper slope of
the local rotation curve) and the value of $V_\odot$ becomes smaller probably
caused by the ridge structure and its signal increasing with distance.


Abstract:
  We propose a two-stage approach Spec PC-CP to identify change points in
multivariate time series. In the first stage, we obtain a low-dimensional
summary of the high-dimensional time series by Spectral Principal Component
Analysis (Spec-PCA). In the second stage, we apply cumulative sum-type test on
the Spectral PCA component using a binary segmentation algorithm. Compared with
existing approaches, the proposed method is able to capture the lead-lag
relationship in time series. Our simulations demonstrate that the Spec PC-CP
method performs significantly better than competing methods for detecting
change points in high-dimensional time series. The results on epileptic seizure
EEG data and stock data also indicate that our new method can efficiently
{detect} change points corresponding to the onset of the underlying events.


Abstract:
  We review the issue of steady spherically symmetric accretion onto a
renormalization group improved Schwarzschild space-time which is solution to an
asymptotically safe theory (AS) containing high-derivative terms. We use a
Hamiltonian dynamical system approach for the analysis of the accretion of four
types of isothermal test fluids: ultra-stiff fluid, ultra-relativistic fluid,
radiation fluid, and sub-relativistic fluid. An important outcome of our study
is that, contrary to what claimed in a recent work, there exist physical
solutions for the accretion of an ultra-relativistic fluid in AS which include
subsonic, supersonic and transonic regimes. We also study quantum corrections
to the known stability of the accretion in general relativity (GR). To this end
we use a perturbative procedure based on the continuity equation with the mass
accretion rate being the perturbed quantity. Two classes of perturbations are
studied: standing waves and travelling waves. We find that quantum gravity
effects either enhance or diminish the stability of the accretion depending on
the type of test fluid and depending on the radial distance to the central
object.


Abstract:
  Einstein-Gauss-Bonnet theory is a string-generated gravity theory when
approaching the low energy limit. By introducing the higher order curvature
terms, this theory is supposed to help to solve the black hole singularity
problem. In this work, we investigate the evaporation of the static spherically
symmetric neutral AdS black holes in Einstein-Gauss-Bonnet gravity in various
spacetime dimensions with both positive and negative couping constant $\alpha$.
By summarizing the asymptotic behavior of the evaporation process, we find the
lifetime of the black holes is dimensional dependent. For $\alpha>0$, in
$D\geqslant6$ cases, the black holes will be completely evaporated in a finite
time, which resembles the Schwarzschild-AdS case in Einstein gravity. While in
$D=4,5$ cases, the black hole lifetime is always infinite, which means the
black hole becomes a remnant in the late time. Remarkably, the cases of
$\alpha>0, D=4,5$ will solve the terminal temperature divergent problem of the
Schwarzschild-AdS case. For $\alpha<0$, in all dimensions, the black hole will
always spend a finite time to a minimal mass corresponding to the smallest
horizon radius $r_{min}=\sqrt{2|\alpha|}$ which coincide with an additional
singularity. This implies that there may exist constraint conditions to the
choice of coupling constant.


Abstract:
  Close-in planets evolve under extreme conditions, raising questions about
their origins and current nature. Two predominant mechanisms are orbital
migration, which brings them close to their star, and atmospheric escape under
the resulting increased irradiation. Yet, their relative roles remain unclear
because we lack models that couple the two mechanisms with high precision on
secular timescales. To address this need, we developed the JADE code, which
simulates the secular atmospheric and dynamical evolution of a planet around
its star, and can include the perturbation induced by a distant third body. On
the dynamical side, the 3D evolution of the orbit is modeled under stellar and
planetary tidal forces, a relativistic correction, and the action of the
distant perturber. On the atmospheric side, the vertical structure of the
atmosphere is integrated over time based on its thermodynamical properties,
inner heating, and the evolving stellar irradiation, which results, in
particular, in photo-evaporation. The JADE code is benchmarked on GJ436 b,
prototype of evaporating giants on eccentric, misaligned orbits at the edge of
the hot Neptunes desert. We confirm that its orbital architecture is well
explained by Kozai migration and unveil a strong interplay between its
atmospheric and orbital evolution. During the resonance phase, the atmosphere
pulsates in tune with the Kozai cycles, which leads to stronger tides and an
earlier migration. This triggers a strong evaporation several Gyr after the
planet formed, refining the paradigm that mass loss is dominant in the early
age of close-in planets. This suggests that the edge of the desert could be
formed of warm Neptunes whose evaporation was delayed by migration. It
strengthens the importance of coupling atmospheric and dynamical evolution over
secular timescales, which the JADE code will allow simulating for a wide range
of systems.


Abstract:
  This paper investigates the performance of different general-game-playing
heuristics for games in the Ludii general game system. Based on these results,
we train several regression learning models to predict the performance of these
heuristics based on each game's description file. We also provide a condensed
analysis of the games available in Ludii, and the different ludemes that define
them.


Abstract:
  This paper studies the identification of causal effects of a continuous
treatment using a new difference-in-difference strategy. Our approach allows
for endogeneity of the treatment, and employs repeated cross-sections. It
requires an exogenous change over time which affects the treatment in a
heterogeneous way, stationarity of the distribution of unobservables and a rank
invariance condition on the time trend. On the other hand, we do not impose any
functional form restrictions or an additive time trend, and we are invariant to
the scaling of the dependent variable. Under our conditions, the time trend can
be identified using a control group, as in the binary difference-in-differences
literature. In our scenario, however, this control group is defined by the
data. We then identify average and quantile treatment effect parameters. We
develop corresponding nonparametric estimators and study their asymptotic
properties. Finally, we apply our results to the effect of disposable income on
consumption.


Abstract:
  The peak power improvement and running safety of petawatt (PW) lasers are
limited by laser-induced damage of optical components with limited sizes and
damage thresholds. Diffraction gratings in pulse compressors have been the
shortest stave of PW lasers up to now, as to manufacture a high quality
meter-sized grating remains particularly challenging. Here, the asymmetric
four-grating compressor (AFGC) with asymmetric configuration is proposed for PW
lasers to increase the maximum bearable output pulse energy and running safety
without neither additional optical component nor extra control in comparison to
a traditional Treacy four-grating compressor (TFGC) with symmetric
configuration. In AFGC, suitable spatial dispersion can be introduced in the
output laser beam which is able to decrease the laser spatial intensity
modulation (LSIM) of the output beam on the final grating. The introduced
spatial dispersion can be automatically compensated at the focal plane by using
the spatiotemporal focusing technique. Based on this simple AFGC design, not
only the damage risk of the final grating can be reduced, but also the maximum
output pulse energy can be improved by about 1.8 times theoretically. As an
example, 100 PW output power can be achieved theoretically by using the AFGC
with an input beam size of 550*700 mm2.


Abstract:
  Let $L(m,n)$ denote Young's lattice consisting of all partitions whose Young
diagrams are contained in the $m\times n$ rectangle. It is a well-known result
that the poset $L(m,n)$ is rank symmetric, rank unimodal, and Sperner. A direct
proof of this result by finding an explicit order matching of $L(m,n)$ is an
outstanding open problem. In this paper, we present an explicit order matching
$\varphi$ for $L(3,n)$ by several different approaches, and give chain tableau
version of $\varphi$ that is very helpful in finding patterns. It is surprise
that the greedy algorithm and a recursive knead process also give the same
order matching. Our methods extend for $L(4,n)$.


Abstract:
  Federated Learning (FL) is a paradigm that aims to support loosely connected
clients in learning a global model collaboratively with the help of a
centralized server. The most popular FL algorithm is Federated Averaging
(FedAvg), which is based on taking weighted average of the client models, with
the weights determined largely based on dataset sizes at the clients. In this
paper, we propose a new approach, termed Federated Node Selection (FedNS), for
the server's global model aggregation in the FL setting. FedNS filters and
re-weights the clients' models at the node/kernel level, hence leading to a
potentially better global model by fusing the best components of the clients.
Using collaborative image classification as an example, we show with
experiments from multiple datasets and networks that FedNS can consistently
achieve improved performance over FedAvg.


Abstract:
  Probabilistic image segmentation encodes varying prediction confidence and
inherent ambiguity in the segmentation problem. While different probabilistic
segmentation models are designed to capture different aspects of segmentation
uncertainty and ambiguity, these modelling differences are rarely discussed in
the context of applications of uncertainty. We consider two common use cases of
segmentation uncertainty, namely assessment of segmentation quality and active
learning. We consider four established strategies for probabilistic
segmentation, discuss their modelling capabilities, and investigate their
performance in these two tasks. We find that for all models and both tasks,
returned uncertainty correlates positively with segmentation error, but does
not prove to be useful for active learning.


Abstract:
  We present quantum algorithms for routing concentration assignments on full
capacity fat-and-slim concentrators, bounded fat-and-slim concentrators, and
regular fat-and-slim concentrators. Classically, the concentration assignment
takes $O(n)$ time on all these concentrators, where $n$ is the number of
inputs. Powered by Grover's quantum search algorithm, our algorithms take
$O(\sqrt{nc}\ln{c})$ time, where $c$ is the capacity of the concentrator. Thus,
our quantum algorithms are asymptotically faster than their classical
counterparts, when $c\ln^2{c}=o(n)$.In general, $c = n^\mu,$ satisfies
$c\ln^2{c}=o(n),$ implying a time complexity of $O(n^{0.5(1+ \mu )} \ln n),$
for any $\mu, 0 < \mu < 1.$


Abstract:
  Electronic structure of one of the nickelates (LaNiO$_{2}$) and one of the
cuprates (CaCuO$_{2}$) is studied with three self consistent GW-based methods:
scGW, sc(GW+Vertex), and quasiparticle self-consistent GW. Low energy features
obtained in our study are in many respects similar to the features reported in
previous DFT+DMFT studies. Consistent with the DFT+DMFT conclusion, we find
LaNiO$_{2}$ as more correlated than CaCuO$_{2}$. However, correlation effects
included in our study change the DFT Fermi surface near the $\Gamma$ point
differently than it was reported in the DMFT studies. Features which are a few
electron-volts away from the Fermi level are broader in our calculations than
in the DFT+DMFT which reflects the differences between the DFT and the GW
methods. Our results are in qualitative agreement with previous G0W0 results,
but the self-consistency brings in the quantitative differences. Generally,
correlation effects are found to be sufficiently weak in both materials which
allows one to use totally ab-initio diagrammatic approaches like sc(GW+Vertex)
and to avoid the methods with adjustable parameters (DFT+U or DFT+DMFT).
However, the possibility of some strong correlations at low energy which cannot
be captured by perturbative methods cannot be completely excluded. For
instance, differences in the Fermi surface should be resolved: experimental
studies are necessary.


Abstract:
  We study the effect of non-universal $Z'$ boson on the rare leptonic decay
modes of $B_s^*$ mesons, mediated by $b arrow sll$ quark transition. Rare B
decays are sensitive to various new physics operators. As B mesons are
composite particles, such decay modes of the excited states are ideal for
probing new physics beyond the standard model. We have constrained our model
parameters from $B_(s,d)-$ {B bar(s,d)}$ mixing data to predict the lepton
polarization asymmetry of $B_(s,d)^*$ arrow ${\mu}^+ {\mu}^-$ channel. It is
found that this asymmetry deviates from its SM prediction along with the
$Z'$effect


Abstract:
  Weyl semimetals exhibit exotic transport responses, among which, recently
Goos-Haenchen (GH) and Imbert-Fedorov (IF) effects have received a revived
attention, which are, otherwise, well-studied phenomena in optical systems and
certain electronic systems. Besides the usual parametric dependence of the
shifts inherited from the underlying Hamiltonian to describe the Weyl system
and/or that induced by external controls, the IF shift further carries a
topological identity -- it depends on the chirality of the Weyl cones. A
realistic system of Weyl semimetal naturally accommodates surface potentials
induced by impurities present on its surface that could pose impediments to
observe clean transport signatures predicted in theoretical models. Classifying
these potentials, we study their effects on GH and IF shifts to provide useful
guidance to future experiments that are tuned to the objective of
characterizing Weyl semimetals and for a possible realisation of novel devices
based on these phenomena. A transfer matrix-based approach is invoked to study
the profile of Weyl wavefunctions across the interface which is hosting the
impurity potentials, revealing that such potentials can lead to several
discerning effects which, in certain cases, extend even to nullifying the IF
shift completely and giving rise to phenomenon like valley inversion.


Abstract:
  The rapid growth of ride-hailing platforms has created a highly competitive
market where businesses struggle to make profits, demanding the need for better
operational strategies. However, real-world experiments are risky and expensive
for these platforms as they deal with millions of users daily. Thus, a need
arises for a simulated environment where they can predict users' reactions to
changes in the platform-specific parameters such as trip fares and incentives.
Building such a simulation is challenging, as these platforms exist within
dynamic environments where thousands of users regularly interact with one
another. This paper presents a framework to mimic and predict user,
specifically driver, behaviors in ride-hailing services. We use a data-driven
hybrid reinforcement learning and imitation learning approach for this. First,
the agent utilizes behavioral cloning to mimic driver behavior using a
real-world data set. Next, reinforcement learning is applied on top of the
pre-trained agents in a simulated environment, to allow them to adapt to
changes in the platform. Our framework provides an ideal playground for
ride-hailing platforms to experiment with platform-specific parameters to
predict drivers' behavioral patterns.


Abstract:
  While multifidelity modeling provides a cost-effective way to conduct
uncertainty quantification with computationally expensive models, much greater
efficiency can be achieved by adaptively deciding the number of required
high-fidelity (HF) simulations, depending on the type and complexity of the
problem and the desired accuracy in the results. We propose a framework for
active learning with multifidelity modeling emphasizing the efficient
estimation of rare events. Our framework works by fusing a low-fidelity (LF)
prediction with an HF-inferred correction, filtering the corrected LF
prediction to decide whether to call the high-fidelity model, and for enhanced
subsequent accuracy, adapting the correction for the LF prediction after every
HF model call. The framework does not make any assumptions as to the LF model
type or its correlations with the HF model. In addition, for improved
robustness when estimating smaller failure probabilities, we propose using
dynamic active learning functions that decide when to call the HF model. We
demonstrate our framework using several academic case studies and two finite
element (FE) model case studies: estimating Navier-Stokes velocities using the
Stokes approximation and estimating stresses in a transversely isotropic model
subjected to displacements via a coarsely meshed isotropic model. Across these
case studies, not only did the proposed framework estimate the failure
probabilities accurately, but compared with either Monte Carlo or a standard
variance reduction method, it also required only a small fraction of the calls
to the HF model.


Abstract:
  Wire-feed laser additive manufacturing is an emerging fabrication technique
capable of highly automated large-scale volume production that can reduce both
material waste and overall cost while improving product lead times. Quality
assurance is necessary for implementation into critical structural
applications. However, the large number of process variables along with the
cost associated with traditional trial and error methods makes this difficult.
This study investigates a comprehensive quality framework based on learning
from experimental data that will enable improved quality control along with
consistent microstructural features of the part. Specifically, a comprehensive
experimental data across multiple process variables and output characteristics
in terms of overall bead quality, geometric shape (i.g. bead height, width,
fusion zone depth, etc.), and microstructural features are collected. The
predicted process-geometry-microstructure relations are then captured by virtue
of data-driven machine learning models. The properties of printed beads are
visualized based on an extensive range of processing space within a
3-dimensional contour map. The insights and impacts of process variables on
bead morphology, geometric and microstructural features are comprehensively
investigated for quality improvement during manufacturing processes.


Abstract:
  Data-free compression raises a new challenge because the original training
dataset for a pre-trained model to be compressed is not available due to
privacy or transmission issues. Thus, a common approach is to compute a
reconstructed training dataset before compression. The current reconstruction
methods compute the reconstructed training dataset with a generator by
exploiting information from the pre-trained model. However, current
reconstruction methods focus on extracting more information from the
pre-trained model but do not leverage network engineering. This work is the
first to consider network engineering as an approach to design the
reconstruction method. Specifically, we propose the AutoReCon method, which is
a neural architecture search-based reconstruction method. In the proposed
AutoReCon method, the generator architecture is designed automatically given
the pre-trained model for reconstruction. Experimental results show that using
generators discovered by the AutoRecon method always improve the performance of
data-free compression.


Abstract:
  Jets and outflows are thought to play important roles in regulating star
formation and disk evolution. HD 163296 is a well-studied Herbig Ae star that
hosts proto-planet candidates, a protoplanetary disk, a protostellar jet, and a
molecular outflow, which makes it an excellent laboratory for studying jets. We
aim to characterize the jet at the inner regions and check if there are large
differences with the features at large separations. A secondary objective is to
demonstrate the performance of Multi Unit Spectroscopic Explorer (MUSE) in
high-contrast imaging of extended line emission. MUSE in the narrow field mode
(NFM) can provide observations at optical wavelengths with high spatial
($\sim$75 mas) and medium spectral ($R\sim$2500) resolution. With the
high-resolution spectral differential imaging (HRSDI) technique, we can
characterize the kinematic structures and physical conditions of jets down to
100 mas. We detect multiple atomic lines in two new knots, B3 and A4, at
distances of <4" from the host star with MUSE. The derived $\dot{M}_{\rm jet} /
\dot{M}_{\rm acc}$ is about 0.08 and 0.06 for knots B3 and A4, respectively.
The observed [Ca II]/[S II] ratios indicate that there is no sign of dust
grains at distances of <4". Assuming the knot A4 traces the streamline, we set
an upper limit of 2.2 au on the size of the launching region. Although MUSE has
the ability to detect the velocity shifts caused by high- and low-velocity
components, we found no significant evidence of velocity decrease transverse to
the jet direction. Our work demonstrates the capability of using MUSE NFM
observations for the detailed study of stellar jets in the optical down to
100~mas. The derived $\dot{M}_{\rm jet} / \dot{M}_{\rm acc}$, no dust grain,
and jet radius at the star support the magneto-centrifugal models as a
launching mechanism for the jet.


Abstract:
  This paper presents the projections on the anomalous neutral triple gauge
couplings ($aNTGC$) via $pp \rightarrow ZZ$ production in the 2$\ell$2$\nu$
final state at a 100 TeV proton-proton collider, \verb"FCC-hh". %100 TeV center
of mass energy of The realistic \verb"FCC-hh" detector environments and its
effects taken into account in the analysis. The study is carried out in the
mode where one Z boson decays into a pair of same-flavor, opposite-sign leptons
(electrons or muons) and the other one decays to the two neutrinos. The new
bounds on the charge-parity (CP)-conserving couplings $C_{\widetilde{B}W} /
\Lambda^{4}$ and CP-violating couplings $C_{WW} / \Lambda^{4}$, $C_{BW} /
\Lambda^{4}$ and $C_{BB} / \Lambda^{4}$ achived at 95\% Confidence Level (C.L.)
using the transverse momentum of the dilepton system ($p_{T}^{\ell \ell}$) are
$[-\, 0.042, \,\, +\,0.042]$, $[-\,0.050, \,\, +\,0.050]$, $[-\,0.050, \,\,
+\,0.050]$, and $[-\,0.048, \,\, +\,0.048]$ in units of TeV$^{-4}$,
respectively.