title:
Sizing Battery Energy Storage and PV System in an Extreme Fast Charging
  Station Considering Uncertainties and Battery Degradation

abstract:
  This paper presents mixed integer linear programming (MILP) formulations to
obtain optimal sizing for a battery energy storage system (BESS) and solar
generation system in an extreme fast charging station (XFCS) to reduce the
annualized total cost. The proposed model characterizes a typical year with
eight representative scenarios and obtains the optimal energy management for
the station and BESS operation to exploit the energy arbitrage for each
scenario. Contrasting extant literature, this paper proposes a constant power
constant voltage (CPCV) based improved probabilistic approach to model the XFCS
charging demand for weekdays and weekends. This paper also accounts for the
monthly and annual demand charges based on realistic utility tariffs.
Furthermore, BESS life degradation is considered in the model to ensure no
replacement is needed during the considered planning horizon. Different from
the literature, this paper offers pragmatic MILP formulations to tally BESS
charge/discharge cycles using the cumulative charge/discharge energy concept.
McCormick relaxations and the Big-M method are utilized to relax the bi-linear
terms in the BESS operational constraints. Finally, a robust optimization-based
MILP model is proposed and leveraged to account for uncertainties in
electricity price, solar generation, and XFCS demand. Case studies were
performed to signify the efficacy of the proposed formulations.


title: Influence of polarization and the environment on wave-particle duality

abstract:
  Wave-particle duality is certainly one of the most curious concepts of
contemporary physics, which ascribes mutually exclusive behaviors to quantum
systems that cannot be observed simultaneously. In the context of two-path
interferometers, these two behaviors are usually described in terms of the
visibility of interference fringes and the path distinguishability. Here, we
use quantum information-theoretic tools to derive quantifiers of these two
properties, which account for the combined influence of path probability and
polarization, and demonstrate that they satisfy a complementarity relation. We
further show that the derived quantities can work as probes in the study of
open quantum dynamics by revealing interesting facets of environment actions,
such as: decoherence, depolarization and scattering.


title:
A Simple Method to Boost Human Pose Estimation Accuracy by Correcting
  the Joint Regressor for the Human3.6m Dataset

abstract:
  Many human pose estimation methods estimate Skinned Multi-Person Linear
(SMPL) models and regress the human joints from these SMPL estimates. In this
work, we show that the most widely used SMPL-to-joint linear layer (joint
regressor) is inaccurate, which may mislead pose evaluation results. To achieve
a more accurate joint regressor, we propose a method to create
pseudo-ground-truth SMPL poses, which can then be used to train an improved
regressor. Specifically, we optimize SMPL estimates coming from a
state-of-the-art method so that its projection matches the silhouettes of
humans in the scene, as well as the ground-truth 2D joint locations. While the
quality of this pseudo-ground-truth is challenging to assess due to the lack of
actual ground-truth SMPL, with the Human 3.6m dataset, we qualitatively show
that our joint locations are more accurate and that our regressor leads to
improved pose estimations results on the test set without any need for
retraining. We release our code and joint regressor at
https://github.com/ubc-vision/joint-regressor-refinement


title: Who's the Expert? On Multi-source Belief Change

abstract:
  Consider the following belief change/merging scenario. A group of information
sources gives a sequence of reports about the state of the world at various
instances (e.g. different points in time). The true states at these instances
are unknown to us. The sources have varying levels of expertise, also unknown
to us, and may be knowledgeable on some topics but not others. This may cause
sources to report false statements in areas they lack expertise. What should we
believe on the basis of these reports? We provide a framework in which to
explore this problem, based on an extension of propositional logic with
expertise formulas. This extended language allows us to express beliefs about
the state of the world at each instance, as well as beliefs about the expertise
of each source. We propose several postulates, provide a couple of families of
concrete operators, and analyse these operators with respect to the postulates.


title:
Perturbative approaches in relativistic kinetic theory and the emergence
  of first-order hydrodynamics

abstract:
  Hydrodynamics can be formulated in terms of a perturbative series in
derivatives of the temperature, chemical potential, and flow velocity around an
equilibrium state. Different formulations for this series have been proposed
over the years, which consequently led to the development of various
hydrodynamic theories. In this work, we discuss the relativistic
generalizations of the perturbative expansions put forward by Chapman and
Enskog, and Hilbert, using general matching conditions in kinetic theory. This
allows us to describe, in a comprehensive way, how different out-of-equilibrium
definitions for the hydrodynamic fields affect the development of the
hydrodynamic perturbative series. We provide a perturbative method for
systematically deriving the hydrodynamic formulation recently proposed by
Bemfica, Disconzi, Noronha, and Kovtun (BDNK) from relativistic kinetic theory.
The various transport coefficients that appear in BDNK (at first-order) are
explicitly computed using a new formulation of the relaxation time
approximation for the Boltzmann equation. Assuming Bjorken flow, we also
determine the hydrodynamic attractors of BDNK theory and compare the overall
hydrodynamic evolution obtained using this formulation with that generated by
the Israel-Stewart equations of motion and also kinetic theory.


title:
"Short on time and big on ideas": Perspectives from Lab Members on
  DIYBio Work in Community Biolabs

abstract:
  DIYbio challenges the status quo by positioning laboratory biology work
outside of traditional institutions. HCI has increasingly explored the DIYbio
movement, but we lack insight into sites of practice such as community biolabs.
Therefore, we gathered data on eleven community biolabs by interviewing sixteen
lab managers and members. These labs represent half of identified organizations
in scope worldwide. Participants detailed their practices and motivations,
outlining the constraints and opportunities of their community biolabs. We
found that lab members conducted technically challenging project work with
access to high-end equipment and professional expertise. We found that the
unique nature of biowork exacerbated challenges for cooperative work, partially
due to the particular time sensitivities of work with living organisms.
Building on our findings, we discuss how community biolab members are creating
new approaches to laboratory biology and how this has design implications for
systems that support non-traditional settings for scientific practice.


title: Strange hadron collectivity in pPb and PbPb collisions

abstract:
  The collective behavior of K$^0_\mathrm{S}$ and $\Lambda/\bar{\Lambda}$
strange hadrons is studied by measuring the elliptic azimuthal anisotropy
($v_2$) using the scalar-product and multiparticle correlation methods.
Proton-lead (pPb) collisions at a nucleon-nucleon center-of-mass energy
$\sqrt{s_\mathrm{NN}}$ = 8.16 TeV and lead-lead (PbPb) collisions at
$\sqrt{s_\mathrm{NN}}$ = 5.02 TeV collected by the CMS experiment at the LHC
are investigated. Nonflow effects in the pPb collisions are studied by using a
subevent cumulant analysis and by excluding events where a jet with transverse
momentum greater than 20\GeV is present. The strange hadron $v_2$ values
extracted in \pPb collisions via the four- and six-particle correlation method
are found to be nearly identical, suggesting the collective behavior.
Comparisons of the pPb and PbPb results for both strange hadrons and charged
particles illustrate how event-by-event flow fluctuations depend on the system
size.


title: FABLE: Fast Approximate Quantum Circuits for Block-Encodings

abstract:
  Block-encodings of matrices have become an essential element of quantum
algorithms derived from the quantum singular value transformation. This
includes a variety of algorithms ranging from the quantum linear systems
problem to quantum walk, Hamiltonian simulation, and quantum machine learning.
Many of these algorithms achieve optimal complexity in terms of black box
matrix oracle queries, but so far the problem of computing quantum circuit
implementations for block-encodings of matrices has been under-appreciated. In
this paper we propose FABLE, a method to generate approximate quantum circuits
for block-encodings of matrices in a fast manner. FABLE circuits have a simple
structure and are directly formulated in terms of one- and two-qubit gates. For
small and structured matrices they are feasible in the NISQ era, and the
circuit parameters can be easily generated for problems up to fifteen qubits.
Furthermore, we show that FABLE circuits can be compressed and sparsified. We
provide a compression theorem that relates the compression threshold to the
error on the block-encoding. We benchmark our method for Heisenberg and Hubbard
Hamiltonians, and Laplacian operators to illustrate that they can be
implemented with a reduced gate complexity without approximation error.


title:
Implementation and validation of realistic (n,x) reaction yields in
  GEANT4 utilizing a detailed evaluated nuclear reaction library below 20 MeV

abstract:
  Neutron-induced reactions with charged particle emission play an important
role in a variety of research fields ranging from fundamental nuclear physics
and nuclear astrophysics to applications of nuclear technologies to energy
production and material science. Recently, the capability to study reactions
with radioactive targets has become important to significantly advance research
in explosive nucleosynthesis and nuclear applications. To achieve the relevant
research goals and study (n,x) reactions over a broad neutron beam energy
range, the Low Energy Neutron-induced charged-particle (Z) chamber (LENZ) at
Los Alamos Neutron Science Center (LANSCE) was developed along with varied
ancillary instrumentation to enable the aforementioned research program. For
the (n,x) reactions of interest at low energies, a precise simulation of the
discrete spectrum of emitted charged particles is essential. In addition, since
LANSCE is a user facility, a simulation application that can be easily
accessible by users has high value. With these goals in mind, we have developed
a detailed simulation using the GEANT4 toolkit. In this work, we present the
implementation and the validation of the simulation using experimental data
from recent campaigns with the LENZ instrument. Specifically, we benchmark the
simulation against a similar MCNP-based tool and determine the realistic range
of applicability for the probability biasing technique used. We describe our
implementation of an evaluated library with angular distribution and partial
cross-section data, and we perform a validation of the application based on
comparisons of simulated spectra with the experimental ones, for a number of
targets used in previous experimental campaigns. Last, we discuss the
limitations, caveats, and assets of the simulation code and techniques used.


title: Vision-based system for a real-time detection and following of UAV

abstract:
  In this paper a vision-based system for detection, motion tracking and
following of Unmanned Aerial Vehicle (UAV) with other UAV (follower) is
presented. For detection of an airborne UAV we apply a convolutional neural
network YOLO trained on a collected and processed dataset of 10,000 images. The
trained network is capable of detecting various multirotor UAVs in indoor,
outdoor and simulation environments. Furthermore, detection results are
improved with Kalman filter which ensures steady and reliable information about
position and velocity of a target UAV. Preserving the target UAV in the field
of view (FOV) and at required distance is accomplished by a simple nonlinear
controller based on visual servoing strategy. The proposed system achieves a
real-time performance on Neural Compute Stick 2 with a speed of 20 frames per
second (FPS) for the detection of an UAV. Justification and efficiency of the
developed vision-based system are confirmed in Gazebo simulation experiment
where the target UAV is executing a 3D trajectory in a shape of number eight.


title: Infusing Linguistic Knowledge of SMILES into Chemical Language Models

abstract:
  The simplified molecular-input line-entry system (SMILES) is the most popular
representation of chemical compounds. Therefore, many SMILES-based molecular
property prediction models have been developed. In particular,
transformer-based models show promising performance because the model utilizes
a massive chemical dataset for self-supervised learning. However, there is no
transformer-based model to overcome the inherent limitations of SMILES, which
result from the generation process of SMILES. In this study, we grammatically
parsed SMILES to obtain connectivity between substructures and their type,
which is called the grammatical knowledge of SMILES. First, we pretrained the
transformers with substructural tokens, which were parsed from SMILES. Then, we
used the training strategy 'same compound model' to better understand SMILES
grammar. In addition, we injected knowledge of connectivity and type into the
transformer with knowledge adapters. As a result, our representation model
outperformed previous compound representations for the prediction of molecular
properties. Finally, we analyzed the attention of the transformer model and
adapters, demonstrating that the proposed model understands the grammar of
SMILES.


title:
Line of Sight Curvature for Missile Guidance using Reinforcement
  Meta-Learning

abstract:
  We use reinforcement meta learning to optimize a line of sight curvature
policy that increases the effectiveness of a guidance system against
maneuvering targets. The policy is implemented as a recurrent neural network
that maps navigation system outputs to a Euler 321 attitude representation. The
attitude representation is then used to construct a direction cosine matrix
that biases the observed line of sight vector. The line of sight rotation rate
derived from the biased line of sight is then mapped to a commanded
acceleration by the guidance system. By varying the bias as a function of
navigation system outputs, the policy enhances accuracy against highly
maneuvering targets. Importantly, our method does not require an estimate of
target acceleration. In our experiments, we demonstrate that when our method is
combined with proportional navigation, the system significantly outperforms
augmented proportional navigation with perfect knowledge of target
acceleration, achieving improved accuracy with less control effort against a
wide range of target maneuvers.


title: Enumerating Connected Dominating Sets

abstract:
  The question to enumerate all inclusion-minimal connected dominating sets in
a graph of order $n$ in time significantly less than $2^n$ is an open question
that was asked in many places. We answer this question affirmatively, by
providing an enumeration algorithm that runs in time $\mathcal{O}(1.9896^n)$,
using polynomial space only. The key to this result is the consideration of
this enumeration problem on 2-degenerate graphs, which is proven to be possible
in time $\mathcal{O}(1.9767^n)$. We also show new lower bound results by
constructing a family of graphs of order $n$ with $\Omega(1.4890^n)$ minimal
connected dominating sets, while previous examples achieved $\Omega(1.4422^n)$.
Our construction results in lower bounds for a few special graph classes.
  We also address essential questions concerning output-sensitive enumeration.
Namely, we give reasons why our algorithm cannot be turned into an enumeration
algorithm that guarantees polynomial delay without much efforts. More
precisely, we prove that it is NP-complete to decide, given a graph $G$ and a
vertex set $U$, if there exists a minimal connected dominating set $D$ with
$U\subseteq D$, even if $G$ is known to be 2-degenerate. Our reduction also
shows that even any subexponential delay is not easy to achieve for enumerating
minimal connected dominating sets. Another reduction shows that no
FPT-algorithms can be expected for this extension problem concerning minimal
connected dominating sets, parameterized by $|U|$. We also relate our
enumeration problem to the famous open Hitting Set Transversal problem, which
can be phrased in our context as the question to enumerate all minimal
dominating sets of a graph with polynomial delay by showing that a
polynomial-delay enumeration algorithm for minimal connected dominating sets
implies an affirmative algorithmic solution to the Hitting Set Transversal
problem.


title: Coherent Control of Evanescent Waves via Beam Shaping

abstract:
  Evanescent waves are central to many technologies such as near-field imaging
that beats the diffraction limit and plasmonic devices. Frustrated total
internal reflection (FTIR) is an experimental method commonly used to study
evanescent waves. In this paper, we shape the incident beam of the FTIR process
with a Mach-Zehnder interferometer and measure light transmittance while
varying the path length difference and interferometric visibility. Our results
show that the transmittance varies with the path length difference and, thus,
the intensity distribution of the shaped beam. Experiment and finite element
method simulation produce results that agree. We also show, through
simulations, that the transmittance can be controlled via other methods of beam
shaping. Our work provides a proof-of-concept demonstration of the coherent
control of the FTIR process, which could lead to advancements in numerous
applications of evanescent waves and FTIR.


title: Classical Optical Analogue of Quantum Discord

abstract:
  Quantum discord has been shown to be a resource for quantum advantage in
addition to quantum entanglement. While many experiments have demonstrated
classical analogies of entanglement, none have done so for discord. We present
a proof-of-concept demonstration for creating a classical analogue of quantum
discord using classical light that takes advantage of the analogy between the
state of two qubits and the spatial modes of a Laguerre-Gauss beam. We
demonstrate the validity of this approach by comparing the intensity profiles
of theoretical simulations to experimental results for different values of
discord. Such a classical analogue of quantum discord may provide further
insight in understanding and development of quantum information technologies
that make use of discord.


title: Sound diffusion with spatiotemporally modulated acoustic metasurfaces

abstract:
  Traditional sound diffusers are quasi-random phase gratings attached to
reflecting surfaces whose purpose is to augment the spatiotemporal incoherence
of the acoustic field scattered from reflective surfaces. This configuration
allows one to cover a large reflecting surface by periodically tiling the
diffuser unit cells to cover a large area while reducing undesirable specular
reflection for incident plane waves. However, the periodic arrangement of the
unit cells leads to coherent constructive and destructive interference in the
scattered field in some directions which is undesirable for achieving acoustic
diffusivity. The spatial uniformity of acoustic energy scattered from
conventional diffusers constructed in this way is a fundamental limitation of
the traditional approach which is not easily overcome when one wishes to cover
large reflecting surfaces. In this work, we investigate spatiotemporal
modulation of the surface acoustic admittance of an acoustic diffuser as a new
approach to improve the sound diffusion. We develop a semi-analytical model
that employs Fourier series expansion to determine the scattered sound field
from a surface admittance consisting of a quadratic residue diffuser (QRD)
design whose individual well admittances are modulated with a traveling wave
with modulation frequency, $\omega_{\mrm{m}}$, amplitude, $Y_\mrm{m}$, and a
wavenumber that matches the unit cell length, $\Lambda$. We observe significant
improvement in diffusion performance due to the fact that the spatiotemporal
modulation scatters sound into additional frequency--wavenumber pairs
associated with harmonics of the modulation frequency and their diffraction
orders. The semi-analytical model results are verified using time-domain finite
element model.


title:
Intervalley electron-hole exchange interaction and impurity-assisted
  recombination of indirect excitons in WS$_2$ and WSe$_2$ monolayers

abstract:
  The variety of excitonic states in tungsten-based dichalcogenide monolayers
stems from unique interplay between the spin and valley degrees of freedom. One
of the exciton species is the indirect exciton (momentum or valley dark), which
is responsible to a series of resonances when the monolayer is charge neutral.
We investigate the short-range electron-hole exchange interaction of the
indirect exciton, as well as its recombination mechanism mediated by
impurities. The analysis provides thorough understanding of the energy and
polarization of the zero-phonon indirect exciton resonance in the emission
spectrum.


title:
Geologic context of the bright MARSIS reflectors in Ultimi Scopuli,
  South Polar Layered Deposits, Mars

abstract:
  Radar-bright basal reflectors have been detected below the South Polar
Layered Deposits (SPLD), Mars using Advanced Radar for Subsurface and
Ionosphere Sounding (MARSIS) data and have an exciting but controversial
interpretation: liquid water from subglacial lakes. We mapped the surface of
the SPLD immediately above and surrounding the putative lakes (1:2M map scale)
in order to provide geologic context for interpretation of the bright basal
reflectors. We use THEMIS daytime IR (100 m/pixel), CTX (6 m/pixel), and HiRISE
(25 cm/pixel) data to characterize geologic units and typical surface
roughness. We find evidence for multiple geologic units with features due to
CO2 and aeolian-related processes. We do not find evidence for surface
modification linked to the postulated lake locations. This is not consistent
with the interpretation of the MARSIS basal radar reflector as subglacial
lakes.


title:
Inverse Multiview I: Multi-Calibrator inverse phase referencing for
  Microarcsecond VLBI Astrometry

abstract:
  Very Long Baseline Interferometry (VLBI) astrometry is a well established
technique for achieving $\pm10~\mu$as parallax accuracies at frequencies well
above 10~GHz. At lower frequencies, uncompensated interferometer delays
associated with the ionosphere play the dominant role in limiting the
astrometric accuracy. Multiview is a novel VLBI calibration method, which uses
observations of multiple quasars to accurately model and remove time-variable,
directional-dependent changes to the interferometer delay. Here we extend the
Multiview technique by phase referencing data to the target source ("inverse
Multiview") and test its performance. Multiple observations with a four-antenna
VLBI array operating at 8.3~GHz show single-epoch astrometric accuracies near
$20~\mu$as for target-reference quasar separations up to about 7 degrees. This
represents an improvement in astrometric accuracy by up to an order of
magnitude compared to standard phase referencing.


title: Bayesian Benefit Risk Analysis

abstract:
  The process of approving and assessing new drugs is often quite complicated,
mainly due to the fact that multiple criteria need to be considered. A standard
way to proceed is with benefit risk analysis, often under the Bayesian paradigm
to account for uncertainty and combine data with expert judgement, which is
operationalised via multi-criteria decision analysis (MCDA) scores. The
procedure is based on a suitable model to accommodate key features of the data,
which are typically of mixed type and potentially depended, with factor models
providing a standard choice. The contribution of this paper is threefold:
first, we extend the family of existing structured factor models. Second, we
provide a framework for choosing between them, which combines fit and
out-of-sample predictive performance. Third, we present a sequential estimation
framework that can offer multiple benefits: (i) it allows us to efficiently
re-estimate MCDA scores of different drugs each time new data become available,
thus getting an idea on potential fluctuations in differences between them,
(ii) it can provide information on potential early stopping in cases of evident
conclusions, thus reducing unnecessary further exposure to undesirable
treatments; (iii) it can potentially allow to assign treatment groups
dynamically based on research objectives. A drawback of sequential estimation
is the increased computational time, but this can be mitigated by efficient
sequential Monte Carlo schemes which we tailor in this paper to the context of
Bayesian benefit risk analysis. The developed methodology is illustrated on
real data on Type II diabetes patients who were administered Metformin (MET),
Rosiglitazone (RSG) and a combination of the two (AVM).


title: Quantum subspace expansion algorithm for Green's functions

abstract:
  We present an algorithm to compute Green's functions on quantum computers for
interacting electron systems, which is a challenging task on conventional
computers. It uses a continued fraction representation based on the Lanczos
method, where the wave functions are expanded as linear combination of basis
states within a quantum subspace. While on conventional computers the cost of
the computation grows exponentially with system size, limiting the method to
small systems, by representing the basis states on a quantum computer one may
overcome this exponential scaling barrier. We propose a two-level multigrid
Trotter time evolution for an efficient preparation of the basis states in a
quantum circuit, which takes advantage of the robustness of the subspace
expansion against Trotter errors. Using a quantum emulator we demonstrate the
algorithm for the Hubbard model on a Bethe lattice with infinite coordination,
which we map to a 16 qubit Anderson impurity model within the dynamical mean
field theory. Our algorithm computes the Green's function accurately for both
the metallic and Mott insulating regimes, with a circuit depth several orders
of magnitude below what has been proposed using time evolution. The two-level
multigrid time evolution reduces the number of Trotter steps required to
compute the Green's function to about four to six. We therefore expect that the
method can be used on near term quantum computers for moderate system sizes,
while allowing for scalability to larger circuit depths and qubit numbers on
future fault tolerant quantum computers.


title:
A Robust, Performance-Portable Discontinuous Galerkin Method for
  Relativistic Hydrodynamics

abstract:
  In this work, we present a discontinuous-Galerkin method for evolving
relativistic hydrodynamics. We include an exploration of analytical and
iterative methods to recover the primitive variables from the conserved
variables for the ideal equation of state and the Taub-Matthews approximation
to the Synge equation of state. We also present a new operator for enforcing a
physically permissible conserved state at all basis points within an element
while preserving the volume average of the conserved state. We implement this
method using the Kokkos performance-portability library to enable running at
performance on both CPUs and GPUs. We use this method to explore the
relativistic Kelvin- Helmholtz instability compared to a finite volume method.
Last, we explore the performance of our implementation on CPUs and GPUs.


title:
Chemotaxis systems with singular sensitivity and logistic source:
  Boundedness, persistence, absorbing set, and entire solutions

abstract:
  This paper deals with the following parabolic-elliptic chemotaxis system with
singular sensitivity and logistic source, $u_t=\Delta u-\chi\nabla\cdot
(\frac{u}{v} \nabla v)+u(a(t,x)-b(t,x) u),$ and $0=\Delta v- \mu v+ \nu u,$
where $\Omega \subset \mathbb{R}^N$ is a smooth bounded domain, $a(t,x)$ and
$b(t,x)$ are positive smooth functions, and $\chi$, $\mu$ and $\nu$ are
positive constants. In the very recent paper, we proved that for every given
nonnegative initial function $0\not\equiv u_0\in C^0(\bar \Omega)$ and $s\in
\mathbb R$, (0.1) has a unique globally defined classical solution
$(u(t,x;s,u_0),v(t,x;s,u_0))$ with $u(0,x;s,u_0)=u_0(x)$, which shows that, in
any space dimensional setting, logistic kinetics prevents the occurrence of
finite-time blow-up even for arbitrarily large $\chi$. We also proved that
globally defined positive solutions of (0.1) are uniformly bounded under some
assumption. In this paper, we further investigate qualitative properties of
globally defined positive solutions of (0.1). Among others, we prove that for
any $q\ge 1$, the $L^q$-norm of any globally defined positive solution is
bounded without any assumption on the parameters in (0.1). Under the assumption
(0.2), we prove that any globally defined positive solution is bounded above
and below eventually by some positive constants independent of its initial
functions. We also prove that (0.1) has a positive entire classical olution
$(u^*(t,x),v^*(t,x))$, which is periodic in $t$ if $a(t,x)$ and $b(t,x)$ are
periodic in $t$ and is independent of $t$ if $a(t,x)$ and $b(t,x)$ are
independent of $t$.


title: Fast and Scalable Human Pose Estimation using mmWave Point Cloud

abstract:
  Millimeter-Wave (mmWave) radar can enable high-resolution human pose
estimation with low cost and computational requirements. However, mmWave data
point cloud, the primary input to processing algorithms, is highly sparse and
carries significantly less information than other alternatives such as video
frames. Furthermore, the scarce labeled mmWave data impedes the development of
machine learning (ML) models that can generalize to unseen scenarios. We
propose a fast and scalable human pose estimation (FUSE) framework that
combines multi-frame representation and meta-learning to address these
challenges. Experimental evaluations show that FUSE adapts to the unseen
scenarios 4$\times$ faster than current supervised learning approaches and
estimates human joint coordinates with about 7 cm mean absolute error.


title: On Unspanned Latent Risks in Dynamic Term Structure Models

abstract:
  We explore the importance of information hidden from the yield curve and
assess how valuable the unspanned risks are to a real-time Bayesian investor
seeking to forecast excess bond returns and maximise her utility. We propose a
novel class of arbitrage-free unspanned Dynamic Term Structure Models (DTSM),
that embed a stochastic market price of risk specification. We develop a
suitable Sequential Monte Carlo (SMC) inferential and prediction scheme that
guarantees joint identification of parameters and latent states and takes into
account all relevant uncertainties. We find that latent factors contain
significant predictive power above and beyond the yield curve, providing
improvement to the out-of-sample predictive performance of models, especially
at shorter maturities. Most importantly, they are capable of exploiting
information hidden from the yield curve and translate the evident statistical
predictability into significant utility gains, out-of-sample. The hidden
component associated with slope risk is countercyclical and links with real
activity.


title:
A New Least Squares Parameter Estimator for Nonlinear Regression
  Equations with Relaxed Excitation Conditions and Forgetting Factor

abstract:
  In this note a new high performance least squares parameter estimator is
proposed. The main features of the estimator are: (i) global exponential
convergence is guaranteed for all identifiable linear regression equations;
(ii) it incorporates a forgetting factor allowing it to preserve alertness to
time-varying parameters; (iii) thanks to the addition of a mixing step it
relies on a set of scalar regression equations ensuring a superior transient
performance; (iv) it is applicable to nonlinearly parameterized regressions
verifying a monotonicity condition and to a class of systems with switched
time-varying parameters; (v) it is shown that it is bounded-input-bounded-state
stable with respect to additive disturbances; (vi) continuous and discrete-time
versions of the estimator are given. The superior performance of the proposed
estimator is illustrated with a series of examples reported in the literature.


title: Veech Groups and Triangulations of Half-Dilation Pillowcases

abstract:
  In this paper we consider the symmetries of triangulable half-dilation
structures on the sphere with four singularities. We show that all such
surfaces can be produced by a tetrahedral construction. Using this
construction, we calculate each such surface's symmetry group in
$\operatorname{PSL}(2, \mathbb{R})$ called the Veech group.


title:
Finite-Frequency Topological Maxwell Modes in Mechanical Self-Dual
  Kagome Lattices

abstract:
  In this Letter, an elastic twisted kagome lattice at a critical twist angle,
called self-dual kagome lattice, is shown to exhibit peculiar finite-frequency
topological modes which emerge when certain conditions are satisfied. These
states are topologically reminiscent to the zero energy (floppy) modes of
Maxwell lattices but they occur at a finite frequency in the band gap of
self-dual kagome lattice. Thus, we present a completely new class of
topological modes which share similarities with both the zero frequency floppy
modes in Maxwell lattices and the finite energy in-gap modes in topological
insulators. We envision the presented mathematical and numerical framework to
be invaluable for many technological advances pertaining to wave phenomenon
such as reconfigurable waveguide designs.


title: Manipulating Elections by Changing Voter Perceptions

abstract:
  The integrity of elections is central to democratic systems. However, a
myriad of malicious actors aspire to influence election outcomes for financial
or political benefit. A common means to such ends is by manipulating
perceptions of the voting public about select candidates, for example, through
misinformation. We present a formal model of the impact of perception
manipulation on election outcomes in the framework of spatial voting theory, in
which the preferences of voters over candidates are generated based on their
relative distance in the space of issues. We show that controlling elections in
this model is, in general, NP-hard, whether issues are binary or real-valued.
However, we demonstrate that critical to intractability is the diversity of
opinions on issues exhibited by the voting public. When voter views lack
diversity, and we can instead group them into a small number of categories --
for example, as a result of political polarization -- the election control
problem can be solved in polynomial time in the number of issues and candidates
for arbitrary scoring rules.


title:
An Approach for Fast Cascading Failure Simulation in Dynamic Models of
  Power Systems

abstract:
  The ground truth for cascading failure in power system can only be obtained
through a detailed dynamic model involving nonlinear differential and algebraic
equations whose solution process is computationally expensive. This has
prohibited adoption of such models for cascading failure simulation. To solve
this, we propose a fast cascading failure simulation approach based on implicit
Backward Euler method (BEM) with stiff decay property. Unfortunately, BEM
suffers from hyperstability issue in case of oscillatory instability and
converges to the unstable equilibrium. We propose a predictor-corrector
approach to fully address the hyperstability issue in BEM. The predictor
identifies oscillatory instability based on eigendecomposition of the system
matrix at the post-disturbance unstable equilibrium obtained as a byproduct of
BEM. The corrector uses right eigenvectors to identify the group of machines
participating in the unstable mode. This helps in applying appropriate
protection schemes as in ground truth. We use Trapezoidal method (TM)-based
simulation as the benchmark to validate the results of the proposed approach on
the IEEE 118-bus network, 2,383-bus Polish grid, and IEEE 68-bus system. The
proposed approach is able to track the cascade path and replicate the end
results of TM-based simulation with very high accuracy while reducing the
average simulation time by approximately 10-35 fold. The proposed approach was
also compared with the partitioned method, which led to similar conclusions.


title:
The Cross-Sectional Intrinsic Entropy. A Comprehensive Stock Market
  Volatility Estimator

abstract:
  To take into account the temporal dimension of uncertainty in stock markets,
this paper introduces a cross-sectional estimation of stock market volatility
based on the intrinsic entropy model. The proposed cross-sectional intrinsic
entropy (CSIE) is defined and computed as a daily volatility estimate for the
entire market, grounded on the daily traded prices: open, high, low, and close
prices (OHLC), along with the daily traded volume for all symbols listed on The
New York Stock Exchange (NYSE) and The National Association of Securities
Dealers Automated Quotations (NASDAQ). We perform a comparative analysis
between the time series obtained from the CSIE and the historical volatility as
provided by the estimators: close-to-close, Parkinson, Garman-Klass,
Rogers-Satchell, Yang-Zhang, and intrinsic entropy (IE), defined and computed
from historical OHLC daily prices of the Standard & Poor's 500 index (S&P500),
Dow Jones Industrial Average (DJIA), and the NASDAQ Composite index,
respectively, for various time intervals. Our study uses approximately 6000 day
reference points, starting on 1 Jan. 2001, until 23 Jan. 2022, for both the
NYSE and the NASDAQ. We found that the CSIE market volatility estimator is
consistently at least 10 times more sensitive to market changes, compared to
the volatility estimate captured through the market indices. Furthermore, beta
values confirm a consistently lower volatility risk for market indices overall,
between 50% and 90% lower, compared to the volatility risk of the entire market
in various time intervals and rolling windows.


title:
The moving crude adiabatic alternative to the adiabatic representation
  in excited state dynamics

abstract:
  The choice of the electronic representation in on-the-fly quantum dynamics is
crucial. The adiabatic representation is appealing since adiabatic states are
readily available from quantum chemistry packages. The nuclear wavepackets are
then expanded in a basis of Gaussian functions, which follow trajectories to
explore the potential energy surfaces and approximate the potential using a
local expansion of the adiabatic quantities. Nevertheless, the adiabatic
representation is plagued with severe limitations when conical intersections
are involved: the diagonal Born-Oppenheimer corrections (DBOCs) are
non-integrable, and the geometric phase effect on the nuclear wavepackets
cannot be accounted for unless a model is available. To circumvent these
difficulties, the moving crude adiabatic (MCA) representation was proposed and
successfully tested in low energy dynamics where the wavepacket skirts the
conical intersection. We assess the MCA representation in the case of
non-adiabatic transitions through conical intersections. First, we show that
using a Gaussian basis in the adiabatic representation indeed exhibits the
aforementioned difficulties with a special emphasis on the possibility to
regularize the DBOC terms. Then, we show that MCA is indeed able to properly
model non-adiabatic transitions. Tests are done on linear vibronic coupling
models for the bis(methylene) adamantyl cation and the butatriene cation.


title:
Controlled Curie temperature, magneto-crystal anisotropy, and valley
  polarization in 2D ferromagnetic Janus 2H-VSeS monolayer

abstract:
  Inspired by the successful synthesis of two$-$dimensional (2D) V$-$based
Janus dichloride monolayers with intrinsic ferromagnetism and high Curie
temperature (T$_{c}$), the electronic structure, spin$-$valley splitting and
magnetic anisotropy of Janus 2H$-$VSeS monolayers are investigated in detailed
using first$-$principles calculations. The results show that the Janus
2H$-$VSeS monolayer exhibits a large valley splitting of 105meV, high T$_{c}$
of 278K and good magnetocrystalline anisotropy (0.31meV) contributed by the
in-plane d$_{x^{2}-y^{2}}$/d$_{xy}$ orbitals of V atoms. The biaxial strain
($-$8%<$\varepsilon$<8%) can effectively tune the magnetic moments of V atom,
valley splitting $\Delta$E, T$_{c}$ and MAE of Janus 2H$-$VSeS monolayer. The
corresponding $\Delta$E and T$_{c}$ are adjusted from 72meV to 106.8meV and
from 180K to 340K, respectively. The electronic phase transition from bipolar
magnetic semiconductor (BMS) to half$-$semiconductor (HSC), spin gapless
semiconductor (SGS), and half$-$metal (HM) is also observed due to the change
of V 3d$-$orbital occupation. Due to the broken space$-$ and time$-$reversal
symmetry, the opposite valley charge carriers carry opposite Berry curvature,
which leads to prominent anomalous Hall conductivity at the K and K' valleys.
The maximum modulation of Berry curvature can reach to 45% and 9.5% by applying
the biaxial strain and charge carrier doping, respectively. The stable
in$-$plane magnetocrystalline anisotropy and large spontaneous valley
polarization make the ferromagnetic Janus 2H$-$VSeS monolayer a promising
material for achieving the spintronics and valleytronics devices.


title:
Bridging Differential Privacy and Byzantine-Robustness via Model
  Aggregation

abstract:
  This paper aims at jointly addressing two seemly conflicting issues in
federated learning: differential privacy (DP) and Byzantine-robustness, which
are particularly challenging when the distributed data are non-i.i.d.
(independent and identically distributed). The standard DP mechanisms add noise
to the transmitted messages, and entangles with robust stochastic gradient
aggregation to defend against Byzantine attacks. In this paper, we decouple the
two issues via robust stochastic model aggregation, in the sense that our
proposed DP mechanisms and the defense against Byzantine attacks have separated
influence on the learning performance. Leveraging robust stochastic model
aggregation, at each iteration, each worker calculates the difference between
the local model and the global one, followed by sending the element-wise signs
to the master node, which enables robustness to Byzantine attacks. Further, we
design two DP mechanisms to perturb the uploaded signs for the purpose of
privacy preservation, and prove that they are $(\epsilon,0)$-DP by exploiting
the properties of noise distributions. With the tools of Moreau envelop and
proximal point projection, we establish the convergence of the proposed
algorithm when the cost function is nonconvex. We analyze the trade-off between
privacy preservation and learning performance, and show that the influence of
our proposed DP mechanisms is decoupled with that of robust stochastic model
aggregation. Numerical experiments demonstrate the effectiveness of the
proposed algorithm.


title: Perceptual Visibility Model for Temporal Contrast Changes in Periphery

abstract:
  Modeling perception is critical for many applications and developments in
computer graphics to optimize and evaluate content generation techniques. Most
of the work to date has focused on central (foveal) vision. However, this is
insufficient for novel wide-field-of-view display devices, such as virtual and
augmented reality headsets. The perceptual models proposed for the fovea do not
readily extend to the off-center, peripheral visual field, where human
perception is drastically different. In this paper, we focus on modeling the
temporal aspect of visual perception in the periphery. We present new
psychophysical experiments that measure the sensitivity of human observers to
different spatio-temporal stimuli across a wide field of view. We use the
collected data to build a perceptual model for the visibility of temporal
changes at different eccentricities in complex video content. Finally, we
discuss, demonstrate, and evaluate a number of of problems that can be
addressed using our technique. First, we show how our model enables injecting
new content in the periphery without distracting the viewer and discuss the
link between the model and human attention. Second, we demonstrate how foveated
rendering methods can be evaluated and optimized to limit the visibility of
temporal aliasing.


title: Intersections and Distinct Intersections in Cross-intersecting Families

abstract:
  Let $\mathcal{F},\mathcal{G}$ be two cross-intersecting families of
$k$-subsets of $\{1,2,\ldots,n\}$. Let $\mathcal{F}\wedge \mathcal{G}$,
$\mathcal{I}(\mathcal{F},\mathcal{G})$ denote the families of all intersections
$F\cap G$ with $F\in \mathcal{F},G\in \mathcal{G}$, and all distinct
intersections $F\cap G$ with $F\neq G, F\in \mathcal{F},G\in \mathcal{G}$,
respectively. For a fixed $T\subset \{1,2,\ldots,n\}$, let $\mathcal{S}_T$ be
the family of all $k$-subsets of $\{1,2,\ldots,n\}$ containing $T$. In the
present paper, we show that $|\mathcal{F}\wedge \mathcal{G}|$ is maximized when
$\mathcal{F}=\mathcal{G}=\mathcal{S}_{\{1\}}$ for $n\geq 2k^2+8k$, while
surprisingly $|\mathcal{I}(\mathcal{F}, \mathcal{G})|$ is maximized when
$\mathcal{F}=\mathcal{S}_{\{1,2\}}\cup \mathcal{S}_{\{3,4\}}\cup
\mathcal{S}_{\{1,4,5\}}\cup \mathcal{S}_{\{2,3,6\}}$ and
$\mathcal{G}=\mathcal{S}_{\{1,3\}}\cup \mathcal{S}_{\{2,4\}}\cup
\mathcal{S}_{\{1,4,6\}}\cup \mathcal{S}_{\{2,3,5\}}$ for $n\geq 100k^2$. The
maximum number of distinct intersections in a $t$-intersecting family is
determined for $n\geq 3(t+2)^3k^2$ as well.


title:
Probabilistic Models with Nonlocal Correlations: Numerical Evidence of
  q-Large Deviation Theory

abstract:
  The correlated probabilistic model introduced and analytically discussed in
Hanel et al (2009) is based on a self-dual transformation of the index $q$
which characterizes a current generalization of Boltzmann-Gibbs statistical
mechanics, namely nonextensive statistical mechanics, and yields, in the
$N\to\infty$ limit, a $Q$-Gaussian distribution for any chosen value of $Q \in
[1,3)$. We show here that, by properly generalizing that self-dual
transformation, it is possible to obtain an entire family of such probabilistic
models, all of them yielding $Q_c$-Gaussians ($Q_c \ge 1$) in the $N\to\infty$
limit. This family turns out to be isomorphic to the Hanel et al model through
a specific monotonic transformation $Q_c(Q)$. Then, by following along the
lines of Tirnakli et al (2022), we numerically show that this family of
correlated probabilistic models provides further evidence towards a
$q$-generalized Large Deviation Theory (LDT), consistently with the Legendre
structure of thermodynamics. The present analysis deepens our understanding of
complex systems (with global correlations among their elements), supporting the
conjecture that generic models whose attractors under summation of $N$
strongly-correlated random variables are $Q$-Gaussians, might always be
concomitantly associated with $q$-exponentials in the LDT sense.


title:
Privacy Sensitive Speech Analysis: Using Federated Learning to Assess
  Depression

abstract:
  Recent studies have used speech signals to assess depression. However, speech
features can lead to serious privacy concerns. To address these concerns, prior
work has used privacy-preserving speech features. However, using a subset of
features can lead to information loss and, consequently, non-optimal model
performance. Furthermore, prior work relies on a centralized approach to
support continuous model updates, posing privacy risks. This paper proposes to
use Federated Learning (FL) to enable decentralized, privacy-preserving speech
analysis to assess depression. Using an existing dataset (DAIC-WOZ), we show
that FL models enable a robust assessment of depression with only 4--6%
accuracy loss compared to a centralized approach. These models also outperform
prior work using the same dataset. Furthermore, the FL models have short
inference latency and small memory footprints while being energy-efficient.
These models, thus, can be deployed on mobile devices for real-time,
continuous, and privacy-preserving depression assessment at scale.


title:
Application of Pontryagin's Maximum Principle to Quantum Metrology in
  Dissipative Systems

abstract:
  Optimal control theory, also known as Pontryagin's Maximum Principle, is
applied to the quantum parameter estimation in the presence of decoherence. An
efficient procedure is devised to compute the gradient of quantum Fisher
information with respect to the control parameters and is used to construct the
optimal control protocol. The proposed procedure keeps the control problem in
the time-invariant form so that both first-order and second-order optimality
conditions derived from Pontryagin's Maximum Principle apply; the second-order
condition turns out to be crucial when the optimal control contains singular
arcs. Concretely we look for the optimal control that maximizes quantum Fisher
information for "twist and turn" problem. We find that the optimal control is
singular without dissipation but can become unbounded once the quantum
decoherence is introduced. An amplitude constraint is needed to guarantee a
bounded solution. With quantum decoherence, the maximum quantum Fisher
information happens at a finite time due to the decoherence, and the asymptotic
value depends on the specific decoherence channel and the control of
consideration.


title:
An ALMA study of the massive molecular clump N159W-North in the Large
  Magellanic Cloud: A possible gas flow penetrating one of the most massive
  protocluster systems in the Local Group

abstract:
  Massive dense clumps in the Large Magellanic Cloud can be an important
laboratory to explore the formation of populous clusters. We report multiscale
ALMA observations of the N159W-North clump, which is the most CO-intense region
in the galaxy. High-resolution CO isotope and 1.3 mm continuum observations
with an angular resolution of $\sim$0."25($\sim$0.07 pc) revealed more than
five protostellar sources with CO outflows within the main ridge clump. One of
the thermal continuum sources, MMS-2, shows especially massive/dense nature
whose total H$_2$ mass and peak column density are $\sim$10$^{4}$ $M_{\odot}$
and $\sim$10$^{24}$ cm$^{-2}$, respectively, and harbors massive ($\sim$100
$M_{\odot}$) starless core candidates identified as its internal substructures.
The main ridge containing this source can be categorized as one of the most
massive protocluster systems in the Local Group. The CO high-resolution
observations found several distinct filamentary clouds extending southward from
the star-forming spots. The CO (1-0) data set with a larger field of view
reveals a conical-shaped, $\sim$30 pc long complex extending toward the
northern direction. These features indicate that a large-scale gas compression
event may have produced the massive star-forming complex. Based on the striking
similarity between the N159W-North complex and the previously reported other
two high-mass star-forming clouds in the nearby regions, we propose a
$"$teardrops inflow model$"$ that explains the synchronized, extreme star
formation across $>$50 pc, including one of the most massive protocluster
clumps in the Local Group.


title:
Fixed Point Theorems for Hypersequences and the Foundation of
  Generalized Differential Geometry I: The Simplified Algebra

abstract:
  Fixed point theorems are one of the many tools used to prove existence and
uniqueness of differential equations. When the data involved contains products
of distributions, some of these tools may not be useful. Thus rises the
necessity to develop new environments and tools capable of handling such
situations. The foundations of a Generalized Differential Geometry is set
having Classical Differential Geometry as a discontinuous subcase, a fixed
point theorem for hypersequences is proved in the context of Colombeau
Generalized Functions and it is shown how it can be used to obtain existence
and uniqueness of differential equations whose data involve products of
distributions. Thus also setting the foundations of a Generalized Analysis. The
strain is also picked up setting the foundations of generalized manifolds and
shown that each classical manifold can be discretely embedded in a generalized
manifold in such a way that the differential structure of the latter is a
natural extension of the differential structure of the former. It is inferred
that $\ {\cal{D}}^{\prime}(\Omega)$ is discretely embedded in $\
{\cal{G}}(\Omega)$, that the elements of $\ C^{\infty}(\Omega)$ form a grid of
equidistant points in $\ {\cal{G}}(\Omega)$ and that association in $\
{\cal{G}}(\Omega)$ is a topological and not an algebraic notion. Ergo,
classical solutions to differential equations are scarce. These achievements
reckoned upon the Generalized Differential Calculus invented by the first
author and his collaborators. Hopefully, Generalized Differential Calculus and
the developments presented in this paper, may be of interest to those working
in Analysis, Applied Mathematics, Geometry and Physics.


title: Challenges to Obtaining Results for Real QCD from SUSY QCD

abstract:
  Recently there have been proposals to understand features of QCD such as
confinement and chiral symmetry breaking by considering supersymmetric versions
of the theory with various patterns of soft breaking. In this note we recall
that with small soft breakings, SUSY QCD is suggestive of observed features of
real QCD. But we outline some of the challenges to establishing these features
of the theory with large soft breakings. It appears difficult to argue for
confinement or chiral symmetry breaking; at best, one can say that {\it if} the
non-supersymmetric theory does not confine and/or break chiral symmetry, phase
transitions would appear inevitable as one increases the soft breakings. We
also discuss, at large N, where confinement would imply chiral symmetry
breaking, the challenges to establishing confinement.


title: Single crystal growth and superconductivity in RbNi$_2$Se$_2$

abstract:
  We report the synthesis and characterization of RbNi$_2$Se$_2$, an analog of
the iron chalcogenide superconductor Rb$_x$Fe$_2$Se$_2$, via transport, angle
resolved photoemission spectroscopy, and density functional theory
calculations. A superconducting transition at $T_{c}$ = 1.20 K is identified.
In normal state, RbNi$_2$Se$_2$ shows paramagnetic and Fermi liquid behaviors.
A large Sommerfeld coefficient yields a heavy effective electron mass of
$m^{*}\approx6m_{e}$. In the superconducting state, zero-field electronic
specific-heat data $C_{es}$ can be described by a two-gap BCS model, indicating
that RbNi$_2$Se$_2$ is a multi-gap superconductor. Our density functional
theory calculations and angle resolved photoemission spectroscopy measurements
demonstrate that RbNi$_2$Se$_2$ exhibits relatively weak correlations and
multi-band characteristics, consistent with the multi-gap superconductivity.


title: A Scalable 5,6-Qubit Grover's Quantum Search Algorithm

abstract:
  Recent studies have been spurred on by the promise of advanced quantum
computing technology, which has led to the development of quantum computer
simulations on classical hardware. Grover's quantum search algorithm is one of
the well-known applications of quantum computing, enabling quantum computers to
perform a database search (unsorted array) and quadratically outperform their
classical counterparts in terms of time. Given the restricted access to
database search for an oracle model (black-box), researchers have demonstrated
various implementations of Grover's circuit for two to four qubits on various
platforms. However, larger search spaces have not yet been explored. In this
paper, a scalable Quantum Grover Search algorithm is introduced and implemented
using 5-qubit and 6-qubit quantum circuits, along with a design pattern for
ease of building an Oracle for a higher order of qubits. For our
implementation, the probability of finding the correct entity is in the high
nineties. The accuracy of the proposed 5-qubit and 6-qubit circuits is
benchmarked against the state-of-the-art implementations for 3-qubit and
4-qubit. Furthermore, the reusability of the proposed quantum circuits using
subroutines is also illustrated by the opportunity for large-scale
implementation of quantum algorithms in the future.


title:
Quantum Approximate Optimization Algorithm with Sparsified Phase
  Operator

abstract:
  The Quantum Approximate Optimization Algorithm (QAOA) is a promising
candidate algorithm for demonstrating quantum advantage in optimization using
near-term quantum computers. However, QAOA has high requirements on gate
fidelity due to the need to encode the objective function in the phase
separating operator, requiring a large number of gates that potentially do not
match the hardware connectivity. Using the MaxCut problem as the target, we
demonstrate numerically that an easier way to implement an alternative phase
operator can be used in lieu of the phase operator encoding the objective
function, as long as the ground state is the same. We observe that if the
ground state energy is not preserved, the approximation ratio obtained by QAOA
with such phase separating operator is likely to decrease. Moreover, we show
that a better alignment of the low energy subspace of the alternative operator
leads to better performance. Leveraging these observations, we propose a
sparsification strategy that reduces the resource requirements of QAOA. We also
compare our sparsification strategy with some other classical graph
sparsification methods, and demonstrate the efficacy of our approach.


title: MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud

abstract:
  Existing general purpose frameworks for gigantic model training, i.e., models
with billions to trillions of parameters, cannot scale efficiently on public
cloud environments due to large communication overheads. In this paper, we
propose MiCS, which Minimizes the Communication Scale to bring down
communication overhead. Specifically, by decreasing the number of participants
in a communication collective, MiCS can utilize existing heterogeneous network
bandwidth on the cloud, reduce network traffic over slower links, and amortize
expensive global gradient synchronization overheads. Our evaluation on AWS
shows that the system throughput of MiCS is up to 2.89$\times$ that of the
state-of-the-art large model training systems. MiCS achieves near-linear
scaling efficiency, which is up to 1.27$\times$ that of DeepSpeed. MiCS allows
us to train a proprietary model with 100 billion parameters on 512 GPUs with
99.4% weak-scaling efficiency, and it is able to saturate over 54.5%
theoretical computation power of each GPU on a public cloud with less GPU
memory and more restricted networks than DGX-A100 clusters.


title:
Implications of the isobar run results for chiral magnetic effect in
  heavy ion collisions

abstract:
  Chiral magnetic effect (CME) is a macroscopic transport phenomenon induced by
quantum anomaly in the presence of chiral imbalance and an external magnetic
field. Relativistic heavy ion collisions provide the unique opportunity to look
for CME in a non-Abelian plasma, where the chiral imbalance is created by
topological transitions similar to those occurring in the Early Universe. The
isobar run at Relativistic Heavy Ion Collider was proposed as a way to separate
the possible CME signal driven by magnetic field from the background. The first
blind analysis results from this important experiment have been recently
released by the STAR Collaboration. Under the pre-defined assumption of
identical background in RuRu and ZrZr, the results are inconsistent with the
presence of CME, as well as with all existing theoretical models (whether
including CME or not). However the observed difference of backgrounds must be
taken into account before any physical conclusion is drawn. In this paper, we
show that once the observed difference in hadron multiplicity and collective
flow are quantitatively taken into account, the STAR results imply a finite CME
signal contribution of about $2\%$.


title: Predict; Do not React for Enabling Efficient Fine Grain DVFS in GPUs

abstract:
  With the continuous improvement of on-chip integrated voltage regulators
(IVRs) and fast, adaptive frequency control, dynamic voltage-frequency scaling
(DVFS) transition times have shrunk from the microsecond to the nanosecond
regime, providing additional opportunities to improve energy efficiency. The
key to unlocking the continued improvement in voltage-frequency circuit
technology is the creation of new, smarter DVFS mechanisms that better adapt to
rapid fluctuations in workload demand.
  It is particularly important to optimize fine-grain DVFS mechanisms for
graphics processing units (GPUs) as the chips become ever more important
workhorses in the datacenter. However, massive amount of thread-level
parallelism in GPUs makes it uniquely difficult to determine the optimal
voltage-frequency state at run-time. Existing solutions-mostly designed for
single-threaded CPUs and longer time scales-fail to consider the seemingly
chaotic, highly varying nature of GPU workloads at short time scales.
  This paper proposes a novel prediction mechanism, PCSTALL, that is tailored
for emerging DVFS capabilities in GPUs and achieves near-optimal energy
efficiency. Using the insights from our fine-grained workload analysis, we
propose a wavefront-level program counter (PC) based DVFS mechanism that
improves program behavior prediction accuracy by 32% on average for a wide set
of GPU applications at 1 microsecond DVFS time epochs. Compared to the current
state-of-art, our PC-based technique achieves 19% average improvement when
optimized for Energy-Delay-Squared Product at 50 microsecond time epochs,
reaching 32% power efficiencies when operated with 1 microsecond DVFS
technologies.


title:
Unsupervised Contrastive Learning based Transformer for Lung Nodule
  Detection

abstract:
  Early detection of lung nodules with computed tomography (CT) is critical for
the longer survival of lung cancer patients and better quality of life.
Computer-aided detection/diagnosis (CAD) is proven valuable as a second or
concurrent reader in this context. However, accurate detection of lung nodules
remains a challenge for such CAD systems and even radiologists due to not only
the variability in size, location, and appearance of lung nodules but also the
complexity of lung structures. This leads to a high false-positive rate with
CAD, compromising its clinical efficacy. Motivated by recent computer vision
techniques, here we present a self-supervised region-based 3D transformer model
to identify lung nodules among a set of candidate regions. Specifically, a 3D
vision transformer (ViT) is developed that divides a CT image volume into a
sequence of non-overlap cubes, extracts embedding features from each cube with
an embedding layer, and analyzes all embedding features with a self-attention
mechanism for the prediction. To effectively train the transformer model on a
relatively small dataset, the region-based contrastive learning method is used
to boost the performance by pre-training the 3D transformer with public CT
images. Our experiments show that the proposed method can significantly improve
the performance of lung nodule screening in comparison with the commonly used
3D convolutional neural networks.


title:
Computational Miniature Mesoscope V2: A deep learning-augmented
  miniaturized microscope for single-shot 3D high-resolution fluorescence
  imaging

abstract:
  Computational Miniature Mesoscope (CM2) is a recently developed computational
imaging system that enables single-shot 3D imaging across a wide field-of-view
(FOV) using a compact optical platform. In this work, we present CM2 V2 - an
advanced CM2 system that integrates novel hardware improvements and a new deep
learning reconstruction algorithm. The platform features a 3D-printed freeform
LED collimator that achieves ~80$\%$ excitation efficiency - a ~3x improvement
over our V1 design, and a hybrid emission filter design that improves the
measurement contrast by >5x. The new computational pipeline includes an
accurate and computationally efficient 3D linear shift-variant (LSV) forward
model and a novel multi-module CM2Net deep learning model. As compared to the
model-based deconvolution in our V1 system, CM2Net achieves ~8x better axial
localization and ~1400x faster reconstruction speed. In addition, CM2Net
consistently achieves high detection performance and superior axial
localization across a wide FOV at a variety of conditions. Trained entirely on
our 3D-LSV simulator generated training data set, CM2Net generalizes well to
real experiments. We experimentally demonstrate that CM2Net achieves accurate
3D reconstruction of fluorescent emitters across a ~7-mm FOV and 800-$\mu$m
depth, and provides ~7-$\mu$m lateral and ~25-$\mu$m axial resolution. We
anticipate that this simple and low-cost computational miniature imaging system
will be impactful to many large-scale 3D fluorescence imaging applications.


title:
Tight analytic bound on the trade-off between device-independent
  randomness and nonlocality

abstract:
  Two parties sharing entangled quantum systems can generate correlations that
cannot be produced using only shared classical resources. These nonlocal
correlations are a fundamental feature of quantum theory but also have
practical applications. For instance, they can be used for device-independent
(DI) random number generation, whose security is certified independently of the
operations performed inside the devices. The amount of certifiable randomness
that can be generated from some given non-local correlations is a key quantity
of interest. Here we derive tight analytic bounds on the maximum certifiable
randomness as a function of the nonlocality as expressed using the
Clauser-Horne-Shimony-Holt (CHSH) value. We show that for every CHSH value
greater than the local value ($2$) and up to $3\sqrt{3}/2\approx2.598$ there
exist quantum correlations with that CHSH value that certify a maximal two bits
of global randomness. Beyond this CHSH value the maximum certifiable randomness
drops. We give a second family of Bell inequalities for CHSH values above
$3\sqrt{3}/2$, and show that they certify the maximum possible randomness for
the given CHSH value. Our work hence provides an achievable upper bound on the
amount of randomness that can be certified for any CHSH value. We illustrate
the robustness of our results, and how they could be used to improve randomness
generation rates in practice, using a Werner state noise model.


title: Quantum Telecloning on NISQ Computers

abstract:
  Due to the no-cloning theorem, generating perfect quantum clones of an
arbitrary quantum state is not possible, however approximate quantum clones can
be constructed. Quantum telecloning is a protocol that originates from a
combination of quantum teleportation and quantum cloning. Here we present $1
\rightarrow 2$ and $1 \rightarrow 3$ quantum telecloning circuits, with and
without ancilla, that are theoretically optimal (meaning the clones are the
highest fidelity allowed by quantum mechanics), universal (meaning the clone
fidelity is independent of the state being cloned), and symmetric (meaning the
clones all have the same fidelity). We implement these circuits on gate model
IBMQ and Quantinuum NISQ hardware and quantify the clone fidelities using
parallel single qubit state tomography. Quantum telecloning using mid-circuit
measurement with real time if statements is demonstrated on the Quantinuum H1-2
device. Two alternative implementations of quantum telecloning (deferred
measurement and post selection) are demonstrated on ibmq\_montreal for cases
where mid-circuit measurement with real time if statements are not available.
Our results show that NISQ devices can achieve near-optimal quantum telecloning
fidelity; for example the Quantinuum H1-2 device running the telecloning
circuits without ancilla achieved a mean clone fidelity of $0.824$ for two
clone circuits and $0.765$ for three clone circuits (the theoretical fidelity
limits are $0.8\bar{33}$ for two clones and $0.\bar{77}$ for three clones).
This demonstrates the viability of performing experimental analysis of quantum
information networks and quantum cryptography protocols on NISQ computers.


title: SciEv: Finding Scientific Evidence Papers for Scientific News

abstract:
  In the past decade, many scientific news media that report scientific
breakthroughs and discoveries emerged, bringing science and technology closer
to the general public. However, not all scientific news article cites proper
sources, such as original scientific papers. A portion of scientific news
articles contain misinterpreted, exaggerated, or distorted information that
deviates from facts asserted in the original papers. Manually identifying
proper citations is laborious and costly. Therefore, it is necessary to
automatically search for pertinent scientific papers that could be used as
evidence for a given piece of scientific news. We propose a system called SciEv
that searches for scientific evidence papers given a scientific news article.
The system employs a 2-stage query paradigm with the first stage retrieving
candidate papers and the second stage reranking them. The key feature of SciEv
is it uses domain knowledge entities (DKEs) to find candidates in the first
stage, which proved to be more effective than regular keyphrases. In the
reranking stage, we explore different document representations for news
articles and candidate papers. To evaluate our system, we compiled a pilot
dataset consisting of 100 manually curated (news,paper) pairs from ScienceAlert
and similar websites. To our best knowledge, this is the first dataset of this
kind. Our experiments indicate that the transformer model performs the best for
DKE extraction. The system achieves a P@1=50%, P@5=71%, and P@10=74% when it
uses a TFIDF-based text representation. The transformer-based re-ranker
achieves a comparable performance but costs twice as much time. We will collect
more data and test the system for user experience.


title: Baba is You is Undecidable

abstract:
  We establish the undecidability of 2019 puzzle game Baba is You through a
reduction from the Post correspondence problem. In particular, we consider a
restricted form of the Post correspondence problem introduced by Neary
(arXiv:1312.6700) that is limited to five pairs of words. Baba is You is an
award winning tile-based game in which the player can reprogram the game's
mechanisms by pushing blocks that spell out the rules. We achieve
undecidability through a generalization of the size of the playfield in the
horizontal direction, adding a "hallway" to one side of the level. The
undecidability of Baba is You has been claimed several times online using
different source problems, including the simulation of Turing machines and
Conway's Game of Life, however, this contribution appears to be the first
formal proof of the result.


title: Examples of compact embedded $\lambda$-hypersurfaces

abstract:
  In the paper, we construct compact embedded $\lambda$-hypersurfaces which are
diffeomorphic to a sphere and are not isometric to a standard sphere. Hence,
one can not expect to have Alexandrov type theorem for $\lambda$-hypersurfaces.


title: Gaze-enhanced Crossmodal Embeddings for Emotion Recognition

abstract:
  Emotional expressions are inherently multimodal -- integrating facial
behavior, speech, and gaze -- but their automatic recognition is often limited
to a single modality, e.g. speech during a phone call. While previous work
proposed crossmodal emotion embeddings to improve monomodal recognition
performance, despite its importance, an explicit representation of gaze was not
included. We propose a new approach to emotion recognition that incorporates an
explicit representation of gaze in a crossmodal emotion embedding framework. We
show that our method outperforms the previous state of the art for both
audio-only and video-only emotion classification on the popular One-Minute
Gradual Emotion Recognition dataset. Furthermore, we report extensive ablation
experiments and provide detailed insights into the performance of different
state-of-the-art gaze representations and integration strategies. Our results
not only underline the importance of gaze for emotion recognition but also
demonstrate a practical and highly effective approach to leveraging gaze
information for this task.


title: ExSum: From Local Explanations to Model Understanding

abstract:
  Interpretability methods are developed to understand the working mechanisms
of black-box models, which is crucial to their responsible deployment.
Fulfilling this goal requires both that the explanations generated by these
methods are correct and that people can easily and reliably understand them.
While the former has been addressed in prior work, the latter is often
overlooked, resulting in informal model understanding derived from a handful of
local explanations. In this paper, we introduce explanation summary (ExSum), a
mathematical framework for quantifying model understanding, and propose metrics
for its quality assessment. On two domains, ExSum highlights various
limitations in the current practice, helps develop accurate model
understanding, and reveals easily overlooked properties of the model. We also
connect understandability to other properties of explanations such as human
alignment, robustness, and counterfactual minimality and plausibility.


title:
Motion Compensated Extreme MRI: Multi-Scale Low Rank Reconstructions for
  Highly Accelerated 3D Dynamic Acquisitions (MoCo-MSLR)

abstract:
  Purpose: To improve upon Extreme MRI, a recently proposed method by Ong Et
al. for reconstructing high spatiotemporal resolution, 3D non-Cartesian
acquisitions by incorporating motion compensation into these reconstructions
using an approach termed MoCo-MSLR. Methods: Motion compensation is challenging
to incorporate into high spatiotemporal resolution reconstruction due to the
memory footprint of the motion fields and the potential to lose dynamics by
relying on an initial high temporal resolution, low spatial resolution
reconstruction. Motivated by the work of Ong Et al. and Huttinga Et al., we
estimate low spatial resolution motion fields through a loss enforced in
k-space and represent these motion fields in a memory efficient manner using
multi-scale low rank components. We interpolate these motion fields to the
desired spatial resolution, and then incorporate these fields into Extreme MRI.
Results: MoCo-MSLR was able to improve image quality for reconstructions around
500ms temporal resolution and capture bulk motion not seen in Extreme MRI.
Further, MoCo-MSLR was able to resolve realistic cardiac dynamics at near 100ms
temporal resolution while Extreme MRI struggled to resolve these dynamics.
Conclusion: MoCo-MSLR improved image quality over Extreme MRI and was able to
resolve both respiratory and cardiac motion in 3D.


title: Learn to Understand Negation in Video Retrieval

abstract:
  Negation is a common linguistic skill that allows human to express what we do
NOT want. Naturally, one might expect video retrieval to support
natural-language queries with negation, e.g., finding shots of kids sitting on
the floor and not playing with the dog. However, the state-of-the-art deep
learning based video retrieval models lack such ability, as they are typically
trained on video description datasets such as MSR-VTT and VATEX that lack
negated descriptions. Their retrieved results basically ignore the negator in
the sample query, incorrectly returning videos showing kids playing with the
dog. In this paper, we present the first study on learning to understand
negation in video retrieval and make contributions as follows. First, by
re-purposing two existing datasets, i.e. MSR-VTT and VATEX, we propose a new
evaluation protocol for testing video retrieval with negation. Second, we
propose a learning based method for training a negation-aware video retrieval
model. The key idea is to first construct a soft negative caption for a
specific training video by partially negating its original caption, and then
compute a bidirectionally constrained loss on the triplet. This auxiliary loss
is then weightedly added to a standard retrieval loss. Experiments on the
re-purposed benchmarks show that re-training the CLIP (Contrastive
Language-Image Pre-Training) model by the proposed method clearly improves its
ability to handle queries with negation. In addition, its performance on the
original benchmarks is also improved. Data and source code will be released.


title:
Avoiding the "Great Filter": An Assessment of Climate Change Solutions
  and Combinations for Effective Implementation

abstract:
  Climate change is the long-term shift in global weather patterns, largely
caused by anthropogenic activity of greenhouse gas emissions. Global climate
temperatures have unmistakably risen and naturally-occurring climate
variability alone cannot account for this trend. Human activities are estimated
to have caused about 1.0 {\deg}C of global warming above the pre-industrial
baseline and if left unchecked, will continue to drastically damage the Earth
and its inhabitants. Globally, natural disasters and subsequent economic losses
have become increasingly impactful as a result of climate change. Both wildlife
ecosystems and human habitats have been negatively impacted, from rising sea
levels to alarming frequency of severe weather events around the world.
Attempts towards alleviating the effects of global warming have often been at
odds and remain divided among a multitude of strategies, reducing the overall
effectiveness of these efforts. It is evident that collaborative action is
required for avoiding the most severe consequences of climate change. This
paper evaluates the main strategies (industrial/energy, political, economic,
agricultural, atmospheric, geological, coastal, and social) towards both
mitigating and adapting to climate change. As well, it provides an optimal
combination of seven solutions which can be implemented simultaneously, working
in tandem to limit and otherwise accommodate the harmful effects of climate
change. Previous legislation and deployment techniques are also discussed as
guides for future endeavors.


title:
Identification of Physical Processes and Unknown Parameters of 3D
  Groundwater Contaminant Problems via Theory-guided U-net

abstract:
  Identification of unknown physical processes and parameters of groundwater
contaminant sources is a challenging task due to their ill-posed and non-unique
nature. Numerous works have focused on determining nonlinear physical processes
through model selection methods. However, identifying corresponding nonlinear
systems for different physical phenomena using numerical methods can be
computationally prohibitive. With the advent of machine learning (ML)
algorithms, more efficient surrogate models based on neural networks (NNs) have
been developed in various disciplines. In this work, a theory-guided U-net
(TgU-net) framework is proposed for surrogate modeling of three-dimensional
(3D) groundwater contaminant problems in order to efficiently elucidate their
involved processes and unknown parameters. In TgU-net, the underlying governing
equations are embedded into the loss function of U-net as soft constraints. For
the considered groundwater contaminant problem, sorption is considered to be a
potential process of an uncertain type, and three equilibrium sorption isotherm
types (i.e., linear, Freundlich, and Langmuir) are considered. Different from
traditional approaches in which one model corresponds to one equation, these
three sorption types are modeled through only one TgU-net surrogate. The three
mentioned sorption terms are integrated into one equation by assigning
indicators. Accurate predictions illustrate the satisfactory generalizability
and extrapolability of the constructed TgU-net. Furthermore, based on the
constructed TgU-net surrogate, a data assimilation method is employed to
identify the physical process and parameters simultaneously. This work shows
the possibility of governing equation discovery of physical problems that
contain multiple and even uncertain processes by using deep learning and data
assimilation methods.


title: Failing to hash into supersingular isogeny graphs

abstract:
  An important open problem in supersingular isogeny-based cryptography is to
produce, without a trusted authority, concrete examples of "hard supersingular
curves," that is, concrete supersingular curves for which computing the
endomorphism ring is as difficult as it is for random supersingular curves. Or,
even better, to produce a hash function to the vertices of the supersingular
$\ell$-isogeny graph which does not reveal the endomorphism ring, or a path to
a curve of known endomorphism ring. Such a hash function would open up
interesting cryptographic applications. In this paper, we document a number of
(thus far) failed attempts to solve this problem, in the hopes that we may spur
further research, and shed light on the challenges and obstacles to this
endeavour. The mathematical approaches contained in this article include: (i)
iterative root-finding for the supersingular polynomial; (ii) gcd's of
specialized modular polynomials; (iii) using division polynomials to create
small systems of equations; (iv) taking random walks in the isogeny graph of
abelian surfaces; and (v) using quantum random walks.


title:
Diagnostic functions of solar coronal magnetic fields from radio
  observations

abstract:
  In solar physics, it is a big challenge to measure the magnetic fields
directly from observations in the upper solar atmosphere, including the
chromosphere and corona. Radio observations are regarded as the most feasible
approach to diagnose the magnetic field in solar chromosphere and corona.
However, because of the complexity and diversity of the emission mechanisms,
the previous studies have only presented the implicit diagnostic functions of
the magnetic field for specific mechanism from solar radio observations. This
work collected and sorted out all methods for diagnosing coronal magnetic field
from solar radio observations, which are expressed as a set of explicit
diagnostic functions. In particular, this work supplemented some important
diagnostic methods missed in other reviews. This set of diagnostic functions
can completely cover all regions of the solar chromosphere and corona,
including the quiet region, active region and flaring source regions. At the
same time, it also includes incoherent radiation such as bremsstrahlung
emission of thermal plasma above the quiet region, cyclotron and
gyro-synchrotron emissions of magnetized hot plasma and mildly relativistic
nonthermal electrons above the active regions, as well as coherently plasma
emission around flaring source regions. Using this set of diagnostic functions
and the related broadband spectral solar radio imaging observations, we can
derive the magnetic fields of almost all regions in the solar atmosphere,which
may help us to make full use of the spectral imaging observations of the new
generation solar radio telescopes (such as MUSER, EVOSA and the future FASR,
etc.) to study the solar activities, and provide a reliable basis for the
prediction of disastrous space weather events.


title: A CT18 global PDF fit at the leading order in QCD

abstract:
  In this paper, we present a CT18 PDFs fitted with Leading-Order QCD
perturbation theory. The CT18 LO PDFs is obtained within the general CT18
framework, along with two additional treatments being imposed to improve the
quality of the fit. We take the $W$-boson charge asymmetry and inclusive
single-top production at LHC as examples to illustrate the implication of the
CT18 LO PDFs.


title: Receiver Design for MIMO Unsourced Random Access with SKP Coding

abstract:
  In this letter, we extend the sparse Kronecker-product (SKP) coding scheme,
originally designed for the additive white Gaussian noise (AWGN) channel, to
multiple input multiple output (MIMO) unsourced random access (URA). With the
SKP coding adopted for MIMO transmission, we develop an efficient Bayesian
iterative receiver design to solve the intended challenging trilinear
factorization problem. Numerical results show that the proposed design
outperforms the existing counterparts, and that it performs well in all
simulated settings with various antenna sizes and active-user numbers.


title:
Nonlinear Least Squares Estimator for Discretely Observed Reflected
  Stochastic Processes

abstract:
  We study the problem of parameter estimation for reflected stochastic
processes driven by a standard Brownian motion. The estimator is obtained using
nonlinear least squares method based on discretely observed processes. Under
some certain conditions, we obtain the consistency and give the asymptotic
distribution of the estimator. Moreover, we briefly remark that our method can
be extended to the one-sided reflected stochastic processes spontaneously.
Numerical studies show that the proposed estimator is adequate for practical
use.


title: Improved Approximation to First-Best Gains-from-Trade

abstract:
  We study the two-agent single-item bilateral trade. Ideally, the trade should
happen whenever the buyer's value for the item exceeds the seller's cost.
However, the classical result of Myerson and Satterthwaite showed that no
mechanism can achieve this without violating one of the Bayesian incentive
compatibility, individual rationality and weakly balanced budget conditions.
This motivates the study of approximating the
trade-whenever-socially-beneficial mechanism, in terms of the expected
gains-from-trade. Recently, Deng, Mao, Sivan, and Wang showed that the
random-offerer mechanism achieves at least a 1/8.23 approximation. We improve
this lower bound to 1/3.15 in this paper. We also determine the exact
worst-case approximation ratio of the seller-pricing mechanism assuming the
distribution of the buyer's value satisfies the monotone hazard rate property.


title:
Nadaraya-Watson estimator for reflected stochastic processes driven by
  Brownian motions

abstract:
  We study the Nadaraya-Watson (N-W) estimator for the drift function of
two-sided reflected stochastic processes. We propose a discrete-type N-W
estimator and a continuous-type N-W estimator based on the discretely observed
processes and continuously observed processes respectively. Under some regular
conditions, we obtain the consistency and give the asymptotic distributions for
the two estimators. Furthermore, we briefly remark that our method can be
applied to the one-sided reflected stochastic processes spontaneously.
Numerical studies show that the proposed estimators is adequate for practical
use.


title: Multimodal Representation Learning With Text and Images

abstract:
  In recent years, multimodal AI has seen an upward trend as researchers are
integrating data of different types such as text, images, speech into modelling
to get the best results. This project leverages multimodal AI and matrix
factorization techniques for representation learning, on text and image data
simultaneously, thereby employing the widely used techniques of Natural
Language Processing (NLP) and Computer Vision. The learnt representations are
evaluated using downstream classification and regression tasks. The methodology
adopted can be extended beyond the scope of this project as it uses
Auto-Encoders for unsupervised representation learning.


title:
Implications from secondary emission from neutral impact on Cassini
  plasma and dust measurements

abstract:
  We investigate the role of secondary electron emission from impact of gas
molecules on the Cassini Langmuir Probe (RPWS-LP) measurements in the
ionosphere of Saturn. We add a model of the secondary electron emission
current, based on laboratory measurements and data from flybys of comet
1P/Halley, and reanalyse the several hundred voltage bias sweeps obtained by
the LP during the Cassini Grand Finale orbits, we find reasonable explanations
for three open conundrums from previous RPWS-LP studies of the Saturn
ionosphere. We find an explanation for the observed positive charging of the
Cassini spacecraft, the possibly overestimated ionospheric electron
temperatures, and the excess ion current reported previously that were used to
estimate dust densities. We also produce an estimate of the water vapour
density from the last six revolutions of Cassini through Saturn's ionosphere,
in tentative agreement with and in higher detail than reported by the neutral
gas monitor Ion and Neutral Mass Spectrometer (INMS). For the sweeps analysed
in detail, we find little evidence that supports ionospheric (positive) ion
densities that are significantly above the electron density in Saturn's
ionosphere, and therefore do not find (indirect or direct) evidence of dust
having a significant charge-carrying role in Saturn's ionosphere. The mixing
ratio estimate from RPWS-LP reveals a highly structured ionosphere in latitude
across all six final revolutions (Rev 288-293), varying with two orders of
magnitude in latitude, as well as one order of magnitude between revolutions
and altitude. The result is generally consistent with an empirical
photochemistry model balancing the production of H+ ions with the H+ loss
through charge transfer with e.g., H2O, CH4 and CO2), for which water vapour
appears as the likeliest and most dominant source of the signal in terms of
yield and concentration.


title:
Nonparametric Estimation for Stochastic Differential Equations Driven by
  Fractional Brownian Motion

abstract:
  We study the nonparametric Nadaraya-Watson estimator of the drift function
for ergodic stochastic processes driven by fractional Brownian motion of Hurst
parameter H > 1/2. The estimator is based on the discretely observed stochastic
processes. By using the ergodic properties and stochastic integral, we obtain
the consistency of the proposed estimator.


title: Topological Thouless pumping in arrays of coupled spin chains

abstract:
  Thouless pumping is a mechanism to perform topologically protected transport
of particles by adiabatically modulating the Hamiltonian. The transported
current is a topological invariant that is intimately related to the integer
quantum Hall effect. Most of the previous works focus on topological pumping in
linear and square lattices. In this work, we theoretically propose a mechanism
to perform topological pumping in arrays of spin chains with complex
geometries. To achieve this, we consider an array where the spin chains are
coupled through their edges, which allows to split the populations to generate
superpositions of spin excitations in different spin chains. We show that due
to the topological protection, the quantum superpositions can be transported
through the array against the effect of disorder. This approach will open a new
avenue to transport excitations and correlated states with potential
applications in quantum technologies and information processing. Our ideas can
be realized in state-of-the-art quantum simulators such as cold atoms and
superconducting qubit arrays.

categories: quant-ph cond-mat.dis-nn cond-mat.mes-hall cond-mat.quant-gas cond-mat.stat-mech


title:
Look Closer to Supervise Better: One-Shot Font Generation via
  Component-Based Discriminator

abstract:
  Automatic font generation remains a challenging research issue due to the
large amounts of characters with complicated structures. Typically, only a few
samples can serve as the style/content reference (termed few-shot learning),
which further increases the difficulty to preserve local style patterns or
detailed glyph structures. We investigate the drawbacks of previous studies and
find that a coarse-grained discriminator is insufficient for supervising a font
generator. To this end, we propose a novel Component-Aware Module (CAM), which
supervises the generator to decouple content and style at a more fine-grained
level, \textit{i.e.}, the component level. Different from previous studies
struggling to increase the complexity of generators, we aim to perform more
effective supervision for a relatively simple generator to achieve its full
potential, which is a brand new perspective for font generation. The whole
framework achieves remarkable results by coupling component-level supervision
with adversarial learning, hence we call it Component-Guided GAN, shortly
CG-GAN. Extensive experiments show that our approach outperforms
state-of-the-art one-shot font generation methods. Furthermore, it can be
applied to handwritten word synthesis and scene text image editing, suggesting
the generalization of our approach.


title:
Operational Adaptation of DNN Classifiers using Elastic Weight
  Consolidation

abstract:
  Autonomous systems (AS) often use Deep Neural Network (DNN) classifiers to
allow them to operate in complex, high dimensional, non-linear, and dynamically
changing environments. Due to the complexity of these environments, DNN
classifiers may output misclassifications due to experiencing new tasks in
their operational environments, which were not identified during development.
Removing a system from operation and retraining it to include the new
identified task becomes economically infeasible as the number of such
autonomous systems increase. Additionally, such misclassifications may cause
financial losses and safety threats to the AS or to other operators in its
environment. In this paper, we propose to reduce such threats by investigating
if DNN classifiers can adapt its knowledge to learn new information in the AS's
operational environment, using only a limited number of observations
encountered sequentially during operation. This allows the AS to adapt to new
encountered information and hence increases the AS's reliability on doing
correct classifications. However, retraining DNNs on different observations
than used in prior training is known to cause catastrophic forgetting or
significant model drift. We investigate if this problem can be controlled by
using Elastic Weight Consolidation (EWC) whilst learning from limited new
observations. We carry out experiments using original and noisy versions of the
MNIST dataset to represent known and new information to DNN classifiers.
Results show that using EWC does make the process of adaptation to new
information a lot more controlled, and thus allowing for reliable adaption of
ASs to new information in their operational environment.


title: To Know by the Company Words Keep and What Else Lies in the Vicinity

abstract:
  The development of state-of-the-art (SOTA) Natural Language Processing (NLP)
systems has steadily been establishing new techniques to absorb the statistics
of linguistic data. These techniques often trace well-known constructs from
traditional theories, and we study these connections to close gaps around key
NLP methods as a means to orient future work. For this, we introduce an
analytic model of the statistics learned by seminal algorithms (including GloVe
and Word2Vec), and derive insights for systems that use these algorithms and
the statistics of co-occurrence, in general. In this work, we derive -- to the
best of our knowledge -- the first known solution to Word2Vec's
softmax-optimized, skip-gram algorithm. This result presents exciting potential
for future development as a direct solution to a deep learning (DL) language
model's (LM's) matrix factorization. However, we use the solution to
demonstrate a seemingly-universal existence of a property that word vectors
exhibit and which allows for the prophylactic discernment of biases in data --
prior to their absorption by DL models. To qualify our work, we conduct an
analysis of independence, i.e., on the density of statistical dependencies in
co-occurrence models, which in turn renders insights on the distributional
hypothesis' partial fulfillment by co-occurrence statistics.

categories: cs.CL cs.IT math.IT physics.data-an stat.ML


title: Quality Assurance in the Context of Contemporary Software Practice

abstract:
  We review the literature on the nature of quality assurance in the context of
comnplex systems developed using iterative and incremental approaches.


title:
Extremal functions for the second-order Sobolev inequality on groups of
  polynomial growth

abstract:
  In this paper, we prove the second-order Sobolev inequalities on Cayley
graphs of groups of polynomial growth. We use the discrete
Concentration-Compactness principle to prove the existence of extremal
functions for best constants in supercritical cases. As applications, we get
the existence of positive ground state solutions to the $p$-biharmonic
equations and the Lane-Emden systems.


title: Local permutation polynomials and the action of e-Klenian groups

abstract:
  Permutation polynomials of finite fields have many applications in Coding
Theory, Cryptography and Combinatorics. In the first part of this paper we
present a new family of local permutation polynomials based on a class of
symmetric subgroups without fixed points, the so called e-Klenian groups. In
the second part we use the fact that bivariate local permutation polynomials
define Latin Squares, to discuss several constructions of Mutually Orthogonal
Latin Squares (MOLS) and, in particular, we provide a new family of MOLS on
size a prime power.