Abstract:
  We show that the nature of the topological fluctuations in $SU(3)$ gauge
theory changes drastically at the finite temperature phase transition. Starting
from temperatures right above the phase transition topological fluctuations
come in well separated lumps of unit charge that form a non-interacting ideal
gas. Our analysis is based on a novel method to count not only the net
topological charge, but also separately the number of positively and negatively
charged lumps in lattice configurations using the spectrum of the overlap Dirac
operator. This enables us to determine the joint distribution of the number of
positively and negatively charged topological objects, and we find this
distribution to be consistent with that of an ideal gas of unit charged
topological objects.


Abstract:
  Measuring the acoustic characteristics of a space is often done by capturing
its impulse response (IR), a representation of how a full-range stimulus sound
excites it. This work generates an IR from a single image, which can then be
applied to other signals using convolution, simulating the reverberant
characteristics of the space shown in the image. Recording these IRs is both
time-intensive and expensive, and often infeasible for inaccessible locations.
We use an end-to-end neural network architecture to generate plausible audio
impulse responses from single images of acoustic environments. We evaluate our
method both by comparisons to ground truth data and by human expert evaluation.
We demonstrate our approach by generating plausible impulse responses from
diverse settings and formats including well known places, musical halls, rooms
in paintings, images from animations and computer games, synthetic environments
generated from text, panoramic images, and video conference backgrounds.


Abstract:
  Lithium-sulfur (Li-S) batteries have become one of the most attractive
alternatives over conventional Li-ion batteries due to their high theoretical
specific energy density (2500 Wh/kg for Li-S vs. $\sim$250 Wh/kg for Li-ion).
Accurate state estimation in Li-S batteries is urgently needed for safe and
efficient operation. To the best of the authors' knowledge, electrochemical
model-based observers have not been reported for Li-S batteries, primarily due
to the complex dynamics that make state observer design a challenging problem.
In this work, we demonstrate a state estimation scheme based on a
zero-dimensional electrochemical model for Li-S batteries. The nonlinear
differential-algebraic equation (DAE) model is incorporated into an extend
Kalman filter. This observer design estimates both differential and algebraic
states that represent the dynamic behavior inside the cell, from voltage and
current measurements only. The effectiveness of the proposed estimation
algorithm is illustrated by numerical simulation results. Our study unlocks how
an electrochemical model can be utilized for practical state estimation of Li-S
batteries.


Abstract:
  We investigate the influence of general forms of disorder on the robustness
of superconductivity in multiband materials. Specifically, we consider a
general two-band system where the bands arise from an orbital degree of freedom
of the electrons. Within the Born approximation, we show that the interplay of
the spin-orbital structure of the normal-state Hamiltonian, disorder
scattering, and superconducting pairing potentials can lead to significant
deviations from the expected robustness of the superconductivity. This can be
conveniently formulated in terms of the so-called "superconducting fitness". In
particular, we verify a key role for unconventional $s$-wave states, permitted
by the spin-orbital structure and which may pair electrons that are not
time-reversed partners. To exemplify the role of Fermi surface topology and
spin-orbital texture, we apply our formalism to the candidate topological
superconductor Cu$_x$Bi$_2$Se$_3$, for which only a single band crosses the
Fermi energy, as well as models of the iron pnictides, which possess multiple
Fermi pockets.


Abstract:
  Classical models for multivariate or spatial extremes are mainly based upon
the asymptotically justified max-stable or generalized Pareto processes. These
models are suitable when asymptotic dependence is present, i.e., the joint tail
decays at the same rate as the marginal tail. However, recent environmental
data applications suggest that asymptotic independence is equally important
and, unfortunately, existing spatial models in this setting that are both
flexible and can be fitted efficiently are scarce. Here, we propose a new
spatial copula model based on the generalized hyperbolic distribution, which is
a specific normal mean-variance mixture and is very popular in financial
modeling. The tail properties of this distribution have been studied in the
literature, but with contradictory results. It turns out that the proofs from
the literature contain mistakes. We here give a corrected theoretical
description of its tail dependence structure and then exploit the model to
analyze a simulated dataset from the inverted Brown-Resnick process, hindcast
significant wave height data in the North Sea, and wind gust data in the state
of Oklahoma, USA. We demonstrate that our proposed model is flexible enough to
capture the dependence structure not only in the tail but also in the bulk.


Abstract:
  Speech enhancement is an essential task of improving speech quality in noise
scenario. Several state-of-the-art approaches have introduced visual
information for speech enhancement,since the visual aspect of speech is
essentially unaffected by acoustic environment. This paper proposes a novel
frameworkthat involves visual information for speech enhancement, by
in-corporating a Generative Adversarial Network (GAN). In par-ticular, the
proposed visual speech enhancement GAN consistof two networks trained in
adversarial manner, i) a generator that adopts multi-layer feature fusion
convolution network to enhance input noisy speech, and ii) a discriminator that
attemptsto minimize the discrepancy between the distributions of the clean
speech signal and enhanced speech signal. Experiment re-sults demonstrated
superior performance of the proposed modelagainst several state-of-the-art


Abstract:
  Recent evidence based on APOGEE data for stars within a few kpc of the
Galactic centre suggests that dissolved globular clusters (GCs) contribute
significantly to the stellar mass budget of the inner halo. In this paper we
enquire into the origins of tracers of GC dissolution, N-rich stars, that are
located in the inner 4 kpc of the Milky Way. From an analysis of the chemical
compositions of these stars we establish that about 30% of the N-rich stars
previously identified in the inner Galaxy may have an accreted origin. This
result is confirmed by an analysis of the kinematic properties of our sample.
The specific frequency of N-rich stars is quite large in the accreted
population, exceeding that of its in situ counterparts by near an order of
magnitude, in disagreement with predictions from numerical simulations. We hope
that our numbers provide a useful test to models of GC formation and
destruction.


Abstract:
  Hypergraphs offer an explicit formalism to describe multibody interactions in
complex systems. To connect dynamics and function in systems with these
higher-order interactions, network scientists have generalised random-walk
models to hypergraphs and studied the multibody effects on flow-based
centrality measures. But mapping the large-scale structure of those flows
requires effective community detection methods. We derive unipartite,
bipartite, and multilayer network representations of hypergraph flows and
explore how they and the underlying random-walk model change the number, size,
depth, and overlap of identified multilevel communities. These results help
researchers choose the appropriate modelling approach when mapping flows on
hypergraphs.


Abstract:
  A number of solar filaments/prominences demonstrate failed eruptions, when a
filament at first suddenly starts to ascend and then decelerates and stops at
some greater height in the corona. The mechanism of the termination of
eruptions is not clear yet. One of the confining forces able to stop the
eruption is the gravity force. Using a simple model of a partial
current-carrying torus loop anchored to the photosphere and photospheric
magnetic field measurements as the boundary condition for the potential
magnetic field extrapolation into the corona, we estimated masses of 15
eruptive filaments. The values of the filament mass show rather wide
distribution in the range of $4\times10^{15}$ -- $270\times10^{16}$g. Masses of
the most of filaments, laying in the middle of the range, are in accordance
with estimations made earlier on the basis of spectroscopic and white-light
observations.


Abstract:
  Heavy-ion therapy, particularly using scanned (active) beam delivery,
provides a precise and highly conformal dose distribution, with maximum dose
deposition for each pencil beam at its endpoint (Bragg peak), and low entrance
and exit dose. To take full advantage of this precision, robust range
verification methods are required; these methods ensure that the Bragg peak is
positioned correctly in the patient and the dose is delivered as prescribed.
Relative range verification allows intra-fraction monitoring of Bragg peak
spacing to ensure full coverage with each fraction, as well as inter-fraction
monitoring to ensure all fractions are delivered consistently. To validate the
proposed filtered Interaction Vertex Imaging method for relative range
verification, a ${}^{16}$O beam was used to deliver 12 Bragg peak positions in
a 40 mm poly-(methyl methacrylate) phantom. Secondary particles produced in the
phantom were monitored using position-sensitive silicon detectors. Events
recorded on these detectors, along with a measurement of the treatment beam
axis, were used to reconstruct the sites of origin of these secondary particles
in the phantom. The distal edge of the depth distribution of these
reconstructed points was determined with logistic fits, and the translation in
depth required to minimize the $\chi^2$ statistic between these fits was used
to compute the range shift between any two Bragg peak positions. In all cases,
the range shift was determined with sub-millimeter precision, to a standard
deviation of the mean of 220(10) $\mu$m. This result validates filtered
Interaction Vertex Imaging as a reliable relative range verification method,
which should be capable of monitoring each energy step in each fraction of a
scanned heavy-ion treatment plan.


Abstract:
  Type IIn supernovae (SNe IIn) are a relatively infrequently observed subclass
of SNe whose photometric and spectroscopic properties are varied. A common
thread among SNe IIn are the complex multiple-component hydrogen Balmer lines.
Owing to the heterogeneity of SNe IIn, online databases contain some outdated,
erroneous, or even contradictory classifications. SN IIn classification is
further complicated by SN impostors and contamination from underlying HII
regions. We have compiled a catalogue of systematically classified nearby
(redshift z < 0.02) SNe IIn using the Open Supernova Catalogue (OSC). We
present spectral classifications for 115 objects previously classified as SNe
IIn. Our classification is based upon results obtained by fitting multiple
Gaussians to the H-alpha profiles. We compare classifications reported by the
OSC and Transient Name Server (TNS) along with the best matched templates from
SNID. We find that 28 objects have been misclassified as SNe IIn. TNS and OSC
can be unreliable; they disagree on the classifications of 51 of the objects
and contain a number of erroneous classifications. Furthermore, OSC and TNS
hold misclassifications for 34 and twelve (respectively) of the transients we
classify as SNe IIn. In total, we classify 87 SNe IIn. We highlight the
importance of ensuring that online databases remain up to date when new or even
contemporaneous data become available. Our work shows the great range of
spectral properties and features that SNe IIn exhibit, which may be linked to
multiple progenitor channels and environment diversity. We set out a
classification sche me for SNe IIn based on the H-alpha profile which is not
greatly affected by the inhomogeneity of SNe IIn.


Abstract:
  Isobaric $^{96}_{44}$Ru+$^{96}_{44}$Ru and $^{96}_{40}$Zr+$^{96}_{40}$Zr
collisions at $\sqrt{s_{_{NN}}}=200$ GeV have been conducted at the
Relativistic Heavy Ion Collider to circumvent the large flow-induced background
in searching for the chiral magnetic effect (CME), predicted by the topological
feature of quantum chromodynamics (QCD). Considering that the background in
isobar collisions is approximately twice that in Au+Au collisions (due to the
smaller multiplicity) and the CME signal is approximately half (due to the
weaker magnetic field), we caution that the CME may not be detectable with the
collected isobar data statistics, within $\sim$2$\sigma$ significance, if the
axial charge per entropy density ($n_5/s$) and the QCD vacuum transition
probability are system independent. This expectation is generally verified by
the Anomalous-Viscous Fluid Dynamics (AVFD) model. While our estimate provides
an approximate "experimental" baseline, theoretical uncertainties on the CME
remain large.


Abstract:
  Complex objects are usually with multiple labels, and can be represented by
multiple modal representations, e.g., the complex articles contain text and
image information as well as multiple annotations. Previous methods assume that
the homogeneous multi-modal data are consistent, while in real applications,
the raw data are disordered, e.g., the article constitutes with variable number
of inconsistent text and image instances. Therefore, Multi-modal Multi-instance
Multi-label (M3) learning provides a framework for handling such task and has
exhibited excellent performance. However, M3 learning is facing two main
challenges: 1) how to effectively utilize label correlation; 2) how to take
advantage of multi-modal learning to process unlabeled instances. To solve
these problems, we first propose a novel Multi-modal Multi-instance Multi-label
Deep Network (M3DN), which considers M3 learning in an end-to-end multi-modal
deep network and utilizes consistency principle among different modal bag-level
predictions. Based on the M3DN, we learn the latent ground label metric with
the optimal transport. Moreover, we introduce the extrinsic unlabeled
multi-modal multi-instance data, and propose the M3DNS, which considers the
instance-level auto-encoder for single modality and modified bag-level optimal
transport to strengthen the consistency among modalities. Thereby M3DNS can
better predict label and exploit label correlation simultaneously. Experiments
on benchmark datasets and real world WKG Game-Hub dataset validate the
effectiveness of the proposed methods.


Abstract:
  Bunch splitting is an RF manipulation method of changing the bunch structure,
bunch numbers and bunch intensity in the high-intensity synchrotrons that serve
as the injector for a particle collider. An efficient way to realize bunch
splitting is to use the combination of different harmonic RF systems, such as
the two-fold bunch splitting of a bunch with a combination of fundamental
harmonic and doubled harmonic RF systems. The two-fold bunch splitting and
three-fold bunch splitting methods have been experimentally verified and
successfully applied to the LHC/PS. In this paper, a generalized multi-fold
bunch splitting method is given. The five-fold bunch splitting method using
specially designed multi-harmonic RF systems was studied and tentatively
applied to the medium-stage synchrotron (MSS), the third accelerator of the
injector chain of the Super Proton-Proton Collider (SPPC), to mitigate the
pileup effects and collective instabilities of a single bunch in the SPPC. The
results show that the five-fold bunch splitting is feasible and both the bunch
population distribution and longitudinal emittance growth after the splitting
are acceptable, e.g., a few percent in the population deviation and less than
10% in the total emittance growth.


Abstract:
  We construct a random unitary Gaussian circuit for continuous-variable (CV)
systems subject to Gaussian measurements. We show that when the measurement
rate is nonzero, the steady state entanglement entropy saturates to an area-law
scaling. This is different from a many-body qubit system, where a generic
entanglement transition is widely expected. Due to the unbounded local Hilbert
space, the time scale to destroy entanglement is always much shorter than the
one to build it, while a balance could be achieved for a finite local Hilbert
space. By the same reasoning, the absence of transition should also hold for
other non-unitary Gaussian CV dynamics.


Abstract:
  Here we report a record thermoelectric power factor of up to 160 $\mu$ W m-1
K-2 for the conjugated polymer poly(3-hexylthiophene) (P3HT). This result is
achieved through the combination of high-temperature rubbing of thin films
together with the use of a large molybdenum dithiolene p-dopant with a high
electron affinity. Comparison of the UV-vis-NIR spectra of the chemically doped
samples to electrochemically oxidized material reveals an oxidation level of
10%, i.e. one polaron for every 10 repeat units. The high power factor arises
due to an increase in the charge-carrier mobility and hence electrical
conductivity along the rubbing direction. We conclude that P3HT, with its
facile synthesis and outstanding processability, should not be ruled out as a
potential thermoelectric material.


Abstract:
  The Python package ComCH is a lightweight specialized computer algebra system
that provides models for well known objects, the surjection and Barratt-Eccles
operads, parameterizing the product structure of algebras that are commutative
in a derived sense. The primary examples of such algebras treated by ComCH are
the cochain complexes of spaces, for which it provides effective constructions
of Steenrod cohomology operations at all prime.


Abstract:
  By probing the population of binary black hole (BBH) mergers detected by
LIGO-Virgo, we can infer properties about the underlying black hole formation
channels. A mechanism known as pair-instability (PI) supernova is expected to
prevent the formation of black holes from stellar collapse with mass greater
than $\sim 40-65\,M_\odot$ and less than $\sim 120\,M_\odot$. Any BBH merger
detected by LIGO-Virgo with a component black hole in this gap, known as the PI
mass gap, likely originated from an alternative formation channel. Here, we
firmly establish GW190521 as an outlier to the stellar-mass BBH population if
the PI mass gap begins at or below $65\, M_{\odot}$. In addition, for a PI
lower boundary of $40-50\, M_{\odot}$, we find it unlikely that the remaining
distribution of detected BBH events, excluding GW190521, is consistent with the
stellar-mass population.


Abstract:
  In this paper, we propose Zero Aware Configurable Data Encoding by Skipping
Transfer (ZAC-DEST), a data encoding scheme to reduce the energy consumption of
DRAM channels, specifically targeted towards approximate computing and error
resilient applications. ZAC-DEST exploits the similarity between recent data
transfers across channels and information about the error resilience behavior
of applications to reduce on-die termination and switching energy by reducing
the number of 1's transmitted over the channels. ZAC-DEST also provides a
number of knobs for trading off the application's accuracy for energy savings,
and vice versa, and can be applied to both training and inference.
  We apply ZAC-DEST to five machine learning applications. On average, across
all applications and configurations, we observed a reduction of $40$% in
termination energy and $37$% in switching energy as compared to the state of
the art data encoding technique BD-Coder with an average output quality loss of
$10$%. We show that if both training and testing are done assuming the presence
of ZAC-DEST, the output quality of the applications can be improved upto 9
times as compared to when ZAC-DEST is only applied during testing leading to
energy savings during training and inference with increased output quality.


Abstract:
  We study two well-known $SU(N)$ chiral gauge theories with fermions in the
symmetric, anti-symmetric and fundamental representations. We give a detailed
description of the global symmetry, including various discrete quotients.
Recent work argues that these theories exhibit a subtle mod 2 anomaly, ruling
out certain phases in which the theories confine without breaking their global
symmetry, leaving a gapless composite fermion in the infra-red. We point out
that no such anomaly exists. We further exhibit an explicit path to the gapless
fermion phase, showing that there is no kinematic obstruction to realising
these phases.


Abstract:
  The nontrivial topology of spin systems such as skyrmions in real space can
promote complex electronic states. Here, we provide a general viewpoint at the
emergence of topological electronic states in spin systems based on the methods
of noncommutative K-theory. By realizing that the structure of the observable
algebra of spin textures is determined by the algebraic properties of the
noncommutative hypertorus, we arrive at a unified understanding of topological
electronic states which we predict to arise in various noncollinear setups. The
power of our approach lies in an ability to categorize emergent topological
states algebraically without referring to smooth real- or reciprocal-space
quantities. This opens a way towards an educated design of topological phases
in aperiodic, disordered, or non-smooth textures of spins and charges
containing topological defects.


Abstract:
  Pairwise alignment of DNA sequencing data is a ubiquitous task in
bioinformatics and typically represents a heavy computational burden. A
standard approach to speed up this task is to compute "sketches" of the DNA
reads (typically via hashing-based techniques) that allow the efficient
computation of pairwise alignment scores. We propose a rate-distortion
framework to study the problem of computing sketches that achieve the optimal
tradeoff between sketch size and alignment estimation distortion. We consider
the simple setting of i.i.d. error-free sources of length $n$ and introduce a
new sketching algorithm called "locational hashing." While standard approaches
in the literature based on min-hashes require $B = (1/D) \cdot O\left( \log n
\right)$ bits to achieve a distortion $D$, our proposed approach only requires
$B = \log^2(1/D) \cdot O(1)$ bits. This can lead to significant computational
savings in pairwise alignment estimation.


Abstract:
  In this article we propose a novel method to estimate the frequency
distribution of linguistic variables while controlling for statistical
non-independence due to shared ancestry. Unlike previous approaches, our
technique uses all available data, from language families large and small as
well as from isolates, while controlling for different degrees of relatedness
on a continuous scale estimated from the data. Our approach involves three
steps: First, distributions of phylogenies are inferred from lexical data.
Second, these phylogenies are used as part of a statistical model to
statistically estimate transition rates between parameter states. Finally, the
long-term equilibrium of the resulting Markov process is computed. As a case
study, we investigate a series of potential word-order correlations across the
languages of the world.


Abstract:
  In a previous work by the author it was shown that every finite dimensional
algebraic structure over an algebraically closed field of characteristic zero K
gives rise to a character $K[X]_{aug}\to K$, where $K[X]_aug$ is a commutative
Hopf algebra that encodes scalar invariants of structures. This enabled us to
think of some characters $K[X]_{aug}\to K$ as algebraic structures with closed
orbit. In this paper we study structures in general symmetric monoidal
categories, and not only in $Vec_K$. We show that every character $\chi :
K[X]_{aug}\to K$ arises from such a structure, by constructing a category
$C_{\chi}$ that is analogous to the universal construction from TQFT. We then
give necessary and sufficient conditions for a given character to arise from a
structure in an abelian category with finite dimensional hom-spaces. We call
such characters good characters. We show that if $\chi$ is good then $C_{\chi}$
is abelian and semisimple, and that the set of good characters forms a
K-algebra. This gives us a way to interpolate algebraic structures, and also
symmetric monoidal categories, in a way that generalizes Deligne's categories
$Rep(S_t)$, $Rep(GL_t(K))$, $Rep(O_t)$, and also some of the symmetric monoidal
categories introduced by Knop. We also explain how one can recover the recent
construction of 2 dimensional TQFT of Khovanov, Ostrik, and Kononov, by the
methods presented here. We give new examples, of interpolations of the
categories $Rep(Aut_{O}(M))$ where $O$ is a discrete valuation ring with a
finite residue field, and M is a finite module over it. We also generalize the
construction of wreath products with $S_t$, which was introduced by Knop.


Abstract:
  We explore the connections between Green's functions for certain differential
equations, covariance functions for Gaussian processes, and the smoothing
splines problem. Conventionally, the smoothing spline problem is considered in
a setting of reproducing kernel Hilbert spaces, but here we present a more
direct approach. With this approach, some choices that are implicit in the
reproducing kernel Hilbert space setting stand out, one example being choice of
boundary conditions and more elaborate shape restrictions.
  The paper first explores the Laplace operator and the Poisson equation and
studies the corresponding Green's functions under various boundary conditions
and constraints. Explicit functional forms are derived in a range of examples.
These examples include several novel forms of the Green's function that, to the
author's knowledge, have not previously been presented. Next we present a
smoothing spline problem where we penalize the integrated squared derivative of
the function to be estimated. We then show how the solution can be explicitly
computed using the Green's function for the Laplace operator. In the last part
of the paper, we explore the connection between Gaussian processes and
differential equations, and show how the Laplace operator is related to
Brownian processes and how processes that arise due to boundary conditions and
shape constraints can be viewed as conditional Gaussian processes. The
presented connection between Green's functions for the Laplace operator and
covariance functions for Brownian processes allows us to introduce several new
novel Brownian processes with specific behaviors. Finally, we consider the
connection between Gaussian process priors and smoothing splines.


Abstract:
  In this paper, we study Toeplitz algebras generated by certain class of
Toeplitz operators on the $p$-Fock space and the $p$-Bergman space with
$1<p<\infty$. Let BUC($\mathbb C^n$) and BUC($\mathbb B_n$) denote the
collections of bounded uniformly continuous functions on $\mathbb C^n$ and
$\mathbb B_n$ (the unit ball in $\mathbb C^n$), respectively. On the $p$-Fock
space, we show that the Toeplitz algebra which has a translation invariant
closed subalgebra of BUC($\mathbb C^n$) as its set of symbols is linearly
generated by Toeplitz operators with the same space of symbols. This answers a
question recently posed by Fulsche \cite{Robert}. On the $p$-Bergman space, we
study Toeplitz algebras with symbols in some translation invariant closed
subalgebras of BUC($\mathbb B_n)$. In particular, we obtain that the Toeplitz
algebra generated by all Toeplitz operators with symbols in BUC($\mathbb B_n$)
is equal to the closed linear space generated by Toeplitz operators with such
symbols. This generalizes the corresponding result for the case of $p=2$
obtained by Xia \cite{Xia2015}.


Abstract:
  We introduce Curvy-an interactive design tool to generate varying density
support structures for 3D printing. Support structures are essential for
printing models with extreme overhangs. Yet, they often cause defects on
contact areas, resulting in poor surface quality. Low-level design of support
structures may alleviate such negative effects. However, it is tedious and
unintuitive for novice users as it is hard to predict the impact of changes to
the support structure on the final printed part. Curvy allows users to define
their high-level preferences on the surface quality directly on the target
object rather than explicitly designing the supports. These preferences are
then automatically translated into low-level design parameters to generate the
support structure. Underlying novel curvy zigzag toolpathing algorithm uses
these instructions to generate varying density supports by altering the spacing
between individual paths in order to achieve prescribed quality. Combined with
the build orientation optimization, Curvy provides a practical solution to the
design of support structures with minimal perceptual or functional impact on
the target part to be printed.


Abstract:
  Brans-Dicke theory contains an additional propagating mode which causes
homogeneous expansion and contraction of test bodies in transverse directions.
This "breathing" mode is associated with novel memory effects in addition to
those of general relativity. Standard tensor mode memories are related to a
symmetry principle: they are determined by the balance equations corresponding
to the BMS symmetries. In this paper, we show that the leading and subleading
breathing memory effects are determined by the balance equations associated
with the leading and "overleading" asymptotic symmetries of a dual formulation
of the scalar field in terms of a two-form gauge field. The memory effect
causes a transition in the vacuum of the dual gauge theory. These results
highlight the significance of dual charges and the physical role of overleading
asymptotic symmetries.


Abstract:
  We give a simple formula for some determinants, and an analogous formula for
pfaffians, both of which are polynomial identities. The second involve some
expressions that interpolate between determinants and pfaffians. We give
several tableau formulas for difference operators in Types A and C, as well as
other operators that appear in the "enriched", or "back-stable" Schubert
polynomials. There are also tableau formulas for the enriched Schubert
polynomials for vexillary and $321$-avoiding permutations.


Abstract:
  There is one-to-one correspondence between quadratic operators (mapping
$\mathbb R^m$ to itself) and cubic matrices. It is known that any quadratic
operator corresponding to a stochastic (in a fixed sense) cubic matrix
preserves the standard simplex. In this paper we find conditions on the
(non-stochastic) cubic matrix ensuring that corresponding quadratic operator
preserves simplex. Moreover, we construct several quadratic non-stochastic
operators which generate chaotic dynamical systems on the simplex. These
chaotic behaviors are \emph{splitted} meaning that the simplex is partitioned
into uncountably many invariant (with respect to quadratic operator) subsets
and the restriction of the dynamical system on each invariant set is chaos in
the sense of Devaney.


Abstract:
  We study the Cauchy problem for the defocusing nonlinear Schr\"odinger (NLS)
equation under the assumption that the solution vanishes as $x \to + \infty$
and approaches an oscillatory plane wave as $x \to -\infty$. We first develop
an inverse scattering transform formalism for solutions satisfying such
step-like boundary conditions. Using this formalism, we prove that there exists
a global solution of the corresponding Cauchy problem and establish a
representation for this solution in terms of the solution of a Riemann-Hilbert
problem. By performing a steepest descent analysis of this Riemann-Hilbert
problem, we identify three asymptotic sectors in the half-plane $t \geq 0$ of
the $xt$-plane and derive asymptotic formulas for the solution in each of these
sectors. Finally, by restricting the constructed solutions to the half-line $x
\geq 0$, we find a class of solutions with asymptotically time-periodic
boundary values previously sought for in the context of the NLS half-line
problem.


Abstract:
  Like other applications in computer vision, medical image segmentation has
been most successfully addressed using deep learning models that rely on the
convolution operation as their main building block. Convolutions enjoy
important properties such as sparse interactions, weight sharing, and
translation equivariance. These properties give convolutional neural networks
(CNNs) a strong and useful inductive bias for vision tasks. In this work we
show that a different method, based entirely on self-attention between
neighboring image patches and without any convolution operations, can achieve
competitive or better results. Given a 3D image block, our network divides it
into $n^3$ 3D patches, where $n=3 \text{ or } 5$ and computes a 1D embedding
for each patch. The network predicts the segmentation map for the center patch
of the block based on the self-attention between these patch embeddings. We
show that the proposed model can achieve segmentation accuracies that are
better than the state of the art CNNs on three datasets. We also propose
methods for pre-training this model on large corpora of unlabeled images. Our
experiments show that with pre-training the advantage of our proposed network
over CNNs can be significant when labeled training data is small.


Abstract:
  We study maximal length collections of disjoint paths, or `disjoint
optimizers', in the directed landscape. We show that disjoint optimizers always
exist, and that their lengths can be used to construct an extended directed
landscape. The extended directed landscape can be built from an independent
collection of extended Airy sheets, which we define from the Airy line
ensemble. We show that the extended directed landscape and disjoint optimizers
are scaling limits of the corresponding objects in Brownian last passage
percolation (LPP). As two consequences of this work, we show that one direction
of the Robinson-Schensted-Knuth bijection passes to the KPZ limit, and we find
a criterion for geodesic disjointness in the directed landscape that uses only
a single Airy line ensemble.
  The proofs rely on a new notion of multi-point LPP across the Airy line
ensemble, combinatorial properties of multi-point LPP, and probabilistic
resampling ideas.


Abstract:
  This paper studies the mixed zero-sum stochastic differential game problem.
We allow the functionals and dynamics to be of polynomial growth. The problem
is formulated as an extended doubly reflected BSDEs with a specific generator.
We show the existence of solution for this doubly reflected BSDEs and we prove
the existence of a saddle-point of the game. Moreover, in the Markovian
framework we prove that the value function is the unique viscosity solution of
the associated Hamilton-Jacobi-Bellman equation.


Abstract:
  We consider the energy complexity of the leader election problem in the
single-hop radio network model, where each device has a unique identifier in
$\{1, 2, \ldots, N\}$. Energy is a scarce resource for small battery-powered
devices. For such devices, most of the energy is often spent on communication,
not on computation. To approximate the actual energy cost, the energy
complexity of an algorithm is defined as the maximum over all devices of the
number of time slots where the device transmits or listens. Much progress has
been made in understanding the energy complexity of leader election in radio
networks, but very little is known about the trade-off between time and energy.
  $\textbf{Time-energy trade-off:}$ For any $k \geq \log \log N$, we show that
a leader among at most $n$ devices can be elected deterministically in $O(k
\cdot n^{1+\epsilon}) + O(k \cdot N^{1/k})$ time and $O(k)$ energy if each
device can simultaneously transmit and listen, where $\epsilon > 0$ is any
small constant. This improves upon the previous $O(N)$-time $O(\log \log
N)$-energy algorithm by Chang et al. [STOC 2017]. We provide lower bounds to
show that the time-energy trade-off of our algorithm is near-optimal.
  $\textbf{Dense instances:}$ For the dense instances where the number of
devices is $n = \Theta(N)$, we design a deterministic leader election algorithm
using only $O(1)$ energy. This improves upon the $O(\log^* N)$-energy algorithm
by Jurdzi\'{n}ski et al. [PODC 2002] and the $O(\alpha(N))$-energy algorithm by
Chang et al. [STOC 2017]. More specifically, we show that the optimal
deterministic energy complexity of leader election is
$\Theta\left(\max\left\{1, \log \frac{N}{n}\right\}\right)$ if the devices
cannot simultaneously transmit and listen, and it is $\Theta\left(\max\left\{1,
\log \log \frac{N}{n}\right\}\right)$ if they can.


Abstract:
  The carbon pump of the world's ocean plays a vital role in the biosphere and
climate of the earth, urging improved understanding of the functions and
influences of the ocean for climate change analyses. State-of-the-art
techniques are required to develop models that can capture the complexity of
ocean currents and temperature flows. This work explores the benefits of using
physics-informed neural networks (PINNs) for solving partial differential
equations related to ocean modeling; such as the Burgers, wave, and
advection-diffusion equations. We explore the trade-offs of using data vs.
physical models in PINNs for solving partial differential equations. PINNs
account for the deviation from physical laws in order to improve learning and
generalization. We observed how the relative weight between the data and
physical model in the loss function influence training results, where small
data sets benefit more from the added physics information.


Abstract:
  We study (2,2s+1) RSOS lattice models deformed by the current-current
operator. Solving the deformed Bethe ansatz equations for the model in the
regime III we find explicit expressions for the ground state energy as well as
for the energy, the momentum and the scattering matrix of the breather-like
excitations. In the scaling limit we get an additional confirmation to the
proposal that the bi-local deformations are a lattice analogue of the TT
perturbations of integrable massive QFTs proposed by Smirnov and Zamolodchikov.


Abstract:
  Multi-step manipulation tasks in unstructured environments are extremely
challenging for a robot to learn. Such tasks interlace high-level reasoning
that consists of the expected states that can be attained to achieve an overall
task and low-level reasoning that decides what actions will yield these states.
We propose a model-free deep reinforcement learning method to learn multi-step
manipulation tasks. We introduce a Robotic Manipulation Network (RoManNet),
which is a vision-based model architecture, to learn the action-value functions
and predict manipulation action candidates. We define a Task Progress based
Gaussian (TPG) reward function that computes the reward based on actions that
lead to successful motion primitives and progress towards the overall task
goal. To balance the ratio of exploration/exploitation, we introduce a Loss
Adjusted Exploration (LAE) policy that determines actions from the action
candidates according to the Boltzmann distribution of loss estimates. We
demonstrate the effectiveness of our approach by training RoManNet to learn
several challenging multi-step robotic manipulation tasks in both simulation
and real-world. Experimental results show that our method outperforms the
existing methods and achieves state-of-the-art performance in terms of success
rate and action efficiency. The ablation studies show that TPG and LAE are
especially beneficial for tasks like multiple block stacking. Code is available
at: https://github.com/skumra/romannet


Abstract:
  This paper deals with the identification of machines in a smart city
environment. The concept of machine biometrics is proposed in this work for the
first time, as a way to authenticate machine identities interacting with humans
in everyday life. This definition is imposed in modern years where autonomous
vehicles, social robots, etc. are considered active members of contemporary
societies. In this context, the case of car identification from the engine
behavioral biometrics is examined. For this purpose, 22 sound features were
extracted and their discrimination capabilities were tested in combination with
9 different machine learning classifiers, towards identifying 5 car
manufacturers. The experimental results revealed the ability of the proposed
biometrics to identify cars with high accuracy up to 98% for the case of the
Multilayer Perceptron (MLP) neural network model.


Abstract:
  Multiparty computation is raising importance because it's primary objective
is to replace any trusted third party in the distributed computation. This work
presents two multiparty shuffling protocols where each party, possesses a
private input, agrees on a random permutation while keeping the permutation
secret. The proposed shuffling protocols are based on permutation network,
thereby data-oblivious. The first proposal is $n\text{-}permute$ that permutes
$n$ inputs in all $n!$ possible ways. $n$-permute network consists of
$2\log{n}-1$ layers, and in each layer there are $n/2$ gates. Our second
protocol is $n_{\pi}$-permute shuffling that defines a permutation set
$\Pi=\{\pi_1,\dots,\pi_N\}$ where $|\Pi| < n!$, and the resultant shuffling is
a random permutation $\pi_i \in \Pi$. The $n_{\pi}$-permute network contains
leases number of layers compare to $n$-permute network. Let $n=n_1n_2$, the
$n_{\pi}$-permute network would define $2\log{n_1}-1+\log{n_2}$ layers. \par
The proposed shuffling protocols are unconditionally secure against malicious
adversary who can corrupt at most $t<n/3$ parties. The probability that
adversary can learn the outcome of $n$-permute is upper bound by
$((n-t)!)^{-1}$. Whereas, the probability that adversary can learn the outcome
of $n_{\pi}$-permute is upper bounded by
$\big(f_{\Pi}(n_1-\theta_1)^{n_2}2^{\theta_2}\big)^{-1}$, for some positive
integer $\theta_1, \theta_2$, and a recursive definition of $f_{\Pi}$. The
protocols allow the parties to build quorums, and distribute the load among the
quorums.


Abstract:
  While an integration by parts formula for the bilinear form of the
hypersingular boundary integral operator for the transient heat equation in
three spatial dimensions is available in the literature, a proof of this
formula seems to be missing. Moreover, the available formula contains an
integral term including the time derivative of the fundamental solution of the
heat equation, whose interpretation is difficult at second glance. To fill
these gaps we provide a rigorous proof of a general version of the integration
by parts formula and an alternative representation of the mentioned integral
term, which is valid for a certain class of functions including the typical
tensor-product discretization spaces.


Abstract:
  Throughput-Outage scaling laws for single-hop cache-aided device-to-device
(D2D) communications have been extensively investigated under the assumption of
the protocol model. However, the corresponding performance under physical
models has not been explored; in particular it remains unclear whether
link-level power control and scheduling can improve the asymptotic performance.
This paper thus investigates the throughput-outage scaling laws of cache-aided
single-hop D2D networks considering a general physical channel model. By
considering the networks with and without the equal-throughput assumption, we
analyze the corresponding outer bounds and provide the achievable performance
analysis. Results show that when the equal-throughput assumption is considered,
using link-level power control and scheduling cannot improve the scaling laws.
On the other hand, when the equal-throughput assumption is not considered, we
show that the proposed double time-slot framework with appropriate link-level
power control and scheduling can significantly improve the throughput-outage
scaling laws, where the fundamental concept is to first distinguish links
according to their communication distances, and then enhance the throughput for
links with small communication distances.


Abstract:
  Beryllium polynitrides, BeN4 is a novel layered material, which has been most
recently fabricated under high pressure (Phys. Rev. Lett. 126(2021), 175501).
As a new class of 2D materials, in this work we conduct first-principles
calculations to examine the stability and explore the electronic nature of MN4
(M= Be, Mg, Ir, Rh, Ni, Cu, Au, Pd, Pt) monolayers. Acquired results confirm
the dynamical and thermal stability of BeN4, MgN4, IrN4, PtN4 and RhN4
monolayers. Interestingly, BeN4 and MgN4 monolayers are found to show
anisotropic Dirac cones in their electronic structure. While PtN4 monolayer is
predicted to be a narrow band-gap semiconductor, IrN4 and RhN4 monolayers are
found to be metallic systems. We also elaborately explore the effects of number
of atomic layers on the electronic features of BeN4 nanosheets, which reveal
highly appealing physics. Our results highlight that BeN4 nanosheet yield
ultrahigh elastic modulus and mechanical strength, outperforming all other
carbon-free 2D materials. Notably, RhN4 nanosheet is predicted to yield high
capacities of 562, 450 and 900 mAh/g, for Li, Na and Ca ions storages,
respectively. This study provides a comprehensive understanding on the
intrinsic properties of MN4 nanosheets and highlight their outstanding physics.


Abstract:
  Semiconductor-superconductor hybrids are commonly used in research on
topological quantum computation. Traditionally, top-down approaches involving
dry or wet etching are used to define the device geometry. These often
aggressive processes risk causing damage to material surfaces, giving rise to
scattering sites particularly problematic for quantum applications. Here, we
propose a method that maintains the flexibility and scalability of selective
area grown nanowire networks while omitting the necessity of etching to create
hybrid segments. Instead, it takes advantage of directional growth methods and
uses bottom-up grown InP structures as shadowing objects to obtain selective
metal deposition. The ability to lithographically define the position and area
of these objects, and to grow a predefined height, ensures precise control of
the shadowed region. We demonstrate the approach by growing InSb nanowire
networks with well-defined Al and Pb islands. Cross-section cuts of the
nanowires reveal a sharp, oxide-free interface between semiconductor and
superconductor. By growing InP structures on both sides of in-plane nanowires,
a combination of Pt and Pb can independently be shadow deposited, enabling a
scalable and reproducible in-situ device fabrication. The
semiconductor-superconductor nanostructures resulting from this approach are at
the forefront of material development for Majorana based experiments.


Abstract:
  Social media radically changed how information is consumed and reported.
Moreover, social networks elicited a disintermediated access to an
unprecedented amount of content. The world health organization (WHO) coined the
term infodemics to identify the information overabundance during an epidemic.
Indeed, the spread of inaccurate and misleading information may alter behaviors
and complicate crisis management and health responses. This paper addresses
information diffusion during the COVID-19 pandemic period with a massive data
analysis on YouTube. First, we analyze more than 2M users' engagement in 13000
videos released by 68 different YouTube channels, with different political bias
and fact-checking indexes. We then investigate the relationship between each
user's political preference and her/his consumption of questionable/reliable
information. Our results, quantified using information theory measures, provide
evidence for the existence of echo chambers across two dimensions represented
by the political bias and by the trustworthiness of information channels.
Finally, we observe that the echo chamber structure cannot be reproduced after
properly randomizing the users' interaction patterns.


Abstract:
  Ni--Ga bilayers are a versatile platform for exploring the competition
between strongly antagonistic ferromagnetic and superconducting phases. We
characterize the impact of this competition on the transport properties of
highly-ballistic Al/Al$ _2 $O$ _3 $(/EuS)/Ni--Ga tunnel junctions from both
experimental and theoretical points of view. While the conductance spectra of
junctions comprising Ni (3 nm)--Ga (60 nm) bilayers can be well understood
within the framework of earlier results, which associate the emerging main
conductance maxima with the junction films' superconducting gaps, thinner Ni
(1.6 nm)--Ga (30 nm) bilayers entail completely different physics, giving rise
to novel conductance-peak subseries that we term conductance shoulders.
Detecting the paramagnetic Meissner response in Ga from polarized neutron
reflectometry provides the essential experimental hint that these conductance
shoulders are caused by superconducting triplet pairings that Ni's
ferromagnetic exchange interaction induces near thin Ni--Ga bilayers'
interfaces -- most likely owing to inhomogeneously magnetized interface
domains. We further substantiate our findings by means of a phenomenological
theoretical model, clarifying that induced superconducting triplet pairings
around the interface of Ni--Ga bilayers can indeed manifest themselves in the
observed conductance shoulders. Arranging our work in a broader context
demonstrates that Ni--Ga-bilayer junctions have a strong potential for
efficient triplet-pairing engineering in superconducting-spintronics
applications.


Abstract:
  When using R package tmbstan for Bayesian inference, the built-in feature
Laplace approximation to the marginal likelihood with random effects integrated
out can be switched on and off. There exists no guideline on whether Laplace
approximation should be used to achieve better efficiency especially when the
statistical model for estimating selection is complicated. To answer this
question, we conducted simulation studies under different scenarios with a
state-space model employing a VAR(1) state equation. We found that turning on
Laplace approximation in tmbstan would probably lower the computational
efficiency, and only when there is a good amount of data, both tmbstan with and
without Laplace approximation are worth trying since in this case, Laplace
approximation is more likely to be accurate and may also lead to slightly
higher computational efficiency. The transition parameters and scale parameters
in a VAR(1) process are hard to be estimated accurately and increasing the
sample size at each time point do not help in the estimation, only more time
points in the data contain more information on these parameters and make the
likelihood dominate the posterior likelihood, thus lead to accurate estimates
for them.


Abstract:
  Our first result provides a new characterization of Auslander algebras using
a property of hereditary torsion pairs. The second result shows an Auslander
algebra $\Lambda$ is left or right glued if and only if $\Lambda$ is
representation-finite. Finally, our third result shows the module category of
any Auslander algebra contains a tilting module with a particular property,
which we call the hereditary property. Applications of this property are
investigated.


Abstract:
  Automated machine learning (AutoML) aims for constructing machine learning
(ML) pipelines automatically. Many studies have investigated efficient methods
for algorithm selection and hyperparameter optimization. However, methods for
ML pipeline synthesis and optimization considering the impact of complex
pipeline structures containing multiple preprocessing and classification
algorithms have not been studied thoroughly. In this paper, we propose a
data-centric approach based on meta-features for pipeline construction and
hyperparameter optimization inspired by human behavior. By expanding the
pipeline search space incrementally in combination with meta-features of
intermediate data sets, we are able to prune the pipeline structure search
space efficiently. Consequently, flexible and data set specific ML pipelines
can be constructed. We prove the effectiveness and competitiveness of our
approach on 28 data sets used in well-established AutoML benchmarks in
comparison with state-of-the-art AutoML frameworks.


Abstract:
  This paper studies computational methods for quasi-stationary distributions
(QSDs). We first proposed a data-driven solver that solves Fokker-Planck
equations for QSDs. Similar as the case of Fokker-Planck equations for
invariant probability measures, we set up an optimization problem that
minimizes the distance from a low-accuracy reference solution, under the
constraint of satisfying the linear relation given by the discretized
Fokker-Planck operator. Then we use coupling method to study the sensitivity of
a QSD against either the change of boundary condition or the diffusion
coefficient. The 1-Wasserstein distance between a QSD and the corresponding
invariant probability measure can be quantitatively estimated. Some numerical
results about both computation of QSDs and their sensitivity analysis are
provided.


Abstract:
  We formulate pure characteristics demand models under uncertainties of
probability distributions as distributionally robust mathematical programs with
stochastic complementarity constraints (DRMP-SCC). For any fixed first-stage
variable and a random realization, the second-stage problem of DRMP-SCC is a
monotone linear complementarity problem (LCP). To deal with uncertainties of
probability distributions of the involved random variables in the stochastic
LCP, we use the distributionally robust approach. Moreover, we propose an
approximation problem with regularization and discretization to solve DRMP-SCC,
which is a two-stage nonconvex-nonconcave minimax optimization problem. We
prove the convergence of the approximation problem to DRMP-SCC regarding the
optimal solution sets, optimal values and stationary points as the
regularization parameter goes to zero and the sample size goes to infinity.
Finally, preliminary numerical results for investigating distributional
robustness of pure characteristics demand models are reported to illustrate the
effectiveness and efficiency of our approaches.


Abstract:
  We explore the connection between the kinematics, structures and stellar
populations of massive galaxies at $0.6<z<1.0$ using the Fundamental Plane
(FP). Combining stellar kinematic data from the Large Early Galaxy Astrophysics
Census (LEGA-C) survey with structural parameters measured from deep Hubble
Space Telescope imaging, we obtain a sample of 1419 massive ($\log(M_*/M_\odot)
>10.5$) galaxies that span a wide range in morphology, star formation activity
and environment, and therefore is representative of the massive galaxy
population at $z\sim0.8$. We find that quiescent and star-forming galaxies
occupy the parameter space of the $g$-band FP differently and thus have
different distributions in the dynamical mass-to-light ratio ($M_{\rm
dyn}/L_g$), largely owing to differences in the stellar age and recent star
formation history, and, to a lesser extent, the effects of dust attenuation. In
contrast, we show that both star-forming and quiescent galaxies lie on the same
mass FP at $z\sim 0.8$, with a comparable level of intrinsic scatter about the
plane. We examine the variation in $M_{\rm dyn}/M_*$ through the thickness of
the mass FP, finding no significant residual correlations with stellar
population properties, S\'ersic index, or galaxy overdensity. Our results
suggest that, at fixed size and velocity dispersion, the variations in $M_{\rm
dyn}/L_g$ of massive galaxies reflect an approximately equal contribution of
variations in $M_*/L_g$, and variations in the dark matter fraction or initial
mass function.


Abstract:
  Series of distributions indexed by equally spaced time points are ubiquitous
in applications and their analysis constitutes one of the challenges of the
emerging field of distributional data analysis. To quantify such distributional
time series, we propose a class of intrinsic autoregressive models that operate
in the space of optimal transport maps. The autoregressive transport models
that we introduce here are based on regressing optimal transport maps on each
other, where predictors can be transport maps from an overall barycenter to a
current distribution or transport maps between past consecutive distributions
of the distributional time series. Autoregressive transport models and
associated distributional regression models specify the link between predictor
and response transport maps by moving along geodesics in Wasserstein space.
These models emerge as natural extensions of the classical autoregressive
models in Euclidean space. Unique stationary solutions of autoregressive
transport models are shown to exist under a geometric moment contraction
condition of Wu and Shao (2004), using properties of iterated random functions.
We also discuss an extension to a varying coefficient coefficient for first
order autoregressive transport models. In addition to simulations, the proposed
models are illustrated with distributional time series of house prices across
U.S. counties and of stock returns across the S&P 500 stock index.


Abstract:
  Pedestrian trajectory prediction for surveillance video is one of the
important research topics in the field of computer vision and a key technology
of intelligent surveillance systems. Social relationship among pedestrians is a
key factor influencing pedestrian walking patterns but was mostly ignored in
the literature. Pedestrians with different social relationships play different
roles in the motion decision of target pedestrian. Motivated by this idea, we
propose a Social Relationship Attention LSTM (SRA-LSTM) model to predict future
trajectories. We design a social relationship encoder to obtain the
representation of their social relationship through the relative position
between each pair of pedestrians. Afterwards, the social relationship feature
and latent movements are adopted to acquire the social relationship attention
of this pair of pedestrians. Social interaction modeling is achieved by
utilizing social relationship attention to aggregate movement information from
neighbor pedestrians. Experimental results on two public walking pedestrian
video datasets (ETH and UCY), our model achieves superior performance compared
with state-of-the-art methods. Contrast experiments with other attention
methods also demonstrate the effectiveness of social relationship attention.


Abstract:
  Adversarial training tends to result in models that are less accurate on
natural (unperturbed) examples compared to standard models. This can be
attributed to either an algorithmic shortcoming or a fundamental property of
the training data distribution, which admits different solutions for optimal
standard and adversarial classifiers. In this work, we focus on the latter case
under a binary Gaussian mixture classification problem. Unlike earlier work, we
aim to derive the natural accuracy gap between the optimal Bayes and
adversarial classifiers, and study the effect of different distributional
parameters, namely separation between class centroids, class proportions, and
the covariance matrix, on the derived gap. We show that under certain
conditions, the natural error of the optimal adversarial classifier, as well as
the gap, are locally minimized when classes are balanced, contradicting the
performance of the Bayes classifier where perfect balance induces the worst
accuracy. Moreover, we show that with an $\ell_\infty$ bounded perturbation and
an adversarial budget of $\epsilon$, this gap is $\Theta(\epsilon^2)$ for the
worst-case parameters, which for suitably small $\epsilon$ indicates the
theoretical possibility of achieving robust classifiers with near-perfect
accuracy, which is rarely reflected in practical algorithms.


Abstract:
  Differential privacy (DP) provides a robust model to achieve privacy
guarantees for released information. We examine the protection potency of
sanitized multi-dimensional frequency distributions via DP randomization
mechanisms against homogeneity attack (HA). HA allows adversaries to obtain the
exact values on sensitive attributes for their targets without having to
identify them from the released data. We propose measures for disclosure risk
from HA and derive closed-form relationships between the privacy loss
parameters in DP and the disclosure risk from HA. The availability of the
closed-form relationships assists understanding the abstract concepts of DP and
privacy loss parameters by putting them in the context of a concrete privacy
attack and offers a perspective for choosing privacy loss parameters when
employing DP mechanisms in information sanitization and release in practice. We
apply the closed-form mathematical relationships in real-life datasets to
demonstrate the assessment of disclosure risk due to HA on differentially
private sanitized frequency distributions at various privacy loss parameters.


Abstract:
  This review presents a concise, yet comprehensive discussion on the evolution
of theoretical methods employed to determine the ground and excited states of
molecules in weak and strong magnetic fields. The weak-field cases have been
studied previously in the context of NMR, where the shielding tensor was
determined by correcting the Diamagnetic Shielding operator up to the second
order. However, the magnetic fields due to the Neutron Stars are extremely high
and cannot be treated perturbatively. Thus, in the interest of the
astrophysical and astrochemical community, this review aims to elaborate on the
computational advancements in quantum mechanics from Hartree-Fock (HF) to
Density Functional Theory (DFT), in the context of molecules in a high (and
ultrahigh) magnetic field. It is found that the mean-field approximation of
electron-electron correlation, as in the case of Hartree-Fock Theory, yields
inaccurate results. On the contrary, CCSD and DFT are found to overcome these
challenges. However, treating eletron-electron correlations in DFT can be
challenging for heavier ions as transition metals. To circumvent this, we
propose the use of DFT/CCSD along with effective Hamiltonian methods, which are
likely to offer physical insights with reasonable accuracy.


Abstract:
  Exponential Puiseux semirings are additive submonoids of $\qq_{\geq 0}$
generated by almost all of the nonnegative powers of a positive rational
number, and they are natural generalizations of rational cyclic semirings. In
this paper, we investigate some of the factorization invariants of exponential
Puiseux semirings and briefly explore the connections of these properties with
semigroup-theoretical invariants. Specifically, we prove that sets of lengths
of atomic exponential Puiseux semirings are almost arithmetic progressions with
a common bound, while unions of sets of lengths are arithmetic progressions.
Additionally, we provide exact formulas to compute the catenary degrees of
these monoids and show that minima and maxima of their sets of distances are
always attained at Betti elements. We conclude by providing various
characterizations of the atomic exponential Puiseux semirings with finite omega
functions; in particular, we completely describe them in terms of their
presentations.


Abstract:
  Kitaev spin liquid (KSL) system has attracted tremendous attention in past
years because of its fundamental significance in condensed matter physics and
promising applications in fault-tolerant topological quantum computation.
Material realization of such a system remains a major challenge in the field
due to the unusual configuration of anisotropic spin interactions, though great
effort has been made before. Here we reveal that rare-earth chalcohalides REChX
(RE=rare earth, Ch=O, S, Se, Te, X=F, Cl, Br, I) can serve as a family of KSL
candidates. Most family members have the typical SmSI-type structure with a
high symmetry of R-3m and rare-earth magnetic ions form an undistorted
honeycomb lattice. The strong spin-orbit coupling of 4f electrons intrinsically
offers anisotropic spin interactions as required by Kitaev model. We have grown
the crystals of YbOCl and synthesized the polycrystals of SmSI, ErOF, HoOF and
DyOF, and made careful structural characterizations. We carry out magnetic and
heat capacity measurements down to 1.8 K and find no obvious magnetic
transition in all the samples but DyOF. The van der Waals interlayer coupling
highlights the true two-dimensionality of the family which is vital for the
exact realization of Abelian/non-Abelian anyons, and the graphene-like feature
will be a prominent advantage for developing miniaturized devices. The family
is expected to act as an inspiring material platform for the exploration of KSL
physics.


Abstract:
  Starting from nonequilibrium quantum field theory on a closed time path, we
derive kinetic equations for the strong-field regime of quantum electrodynamics
(QED) using a systematic expansion in the gauge coupling $e$. The strong field
regime is characterized by a large photon field of order $\mathcal{O}(1/e)$,
which is relevant for the description of, e.g., intense laser fields, the
initial stages of off-central heavy ion collisions, and condensed matter
systems with net fermion number. The strong field enters the dynamical
equations via both quantum Vlasov and collision terms, which we derive to order
$\mathcal{O}(e^2)$. The kinetic equations feature generalized scattering
amplitudes that have their own equation of motion in terms of the fermion
spectral function. The description includes single photon emission,
electron-positron pair photoproduction, vacuum (Schwinger) pair production,
their inverse processes, medium effects and contributions from the field, which
are not restricted to the so-called locally-constant crossed field
approximation. This extends known kinetic equations commonly used in
strong-field QED of intense laser fields. In particular, we derive an
expression for the asymptotic fermion pair number that includes leading-order
collisions and remains valid for strongly inhomogeneous fields. For the purpose
of analytically highlighting limiting cases, we also consider plane-wave fields
for which it is shown how to recover Furry-picture scattering amplitudes by
further assuming negligible occupations. Known on-shell descriptions are
recovered in the case of simply peaked ultrarelativistic fermion occupations.
Collisional strong-field equations are necessary to describe the dynamics to
thermal equilibrium starting from strong-field initial conditions.


Abstract:
  We present an explanation system for applications that leverage Answer Set
Programming (ASP). Given a program P, an answer set A of P, and an atom a in
the program P, our system generates all explanation graphs of a which help
explain why a is true (or false) given the program P and the answer set A. We
illustrate the functionality of the system using some examples from the
literature.


Abstract:
  The presence of constant power loads (CPLs) in dc shipboard microgrids may
lead to unstable conditions. The present work investigates the stability
properties of dc microgrids where CPLs are fed by fuel cells (FCs), and energy
storage systems (ESSs) equipped with voltage droop control. With respect to the
previous literature, the dynamics of the duty cycles of the dc-dc converters
implementing the droop regulation are considered. A mathematical model has been
derived, and tuned to best mimic the behavior of the electrical representation
implemented in DIgSILENT. Then the model is used to find the sufficient
conditions for stability with respect to the droop coefficient, the dc-bus
capacitor, and the inductances of the dc-dc converters.


Abstract:
  We embed Nelson's stochastic quantization in the Schwartz-Meyer second order
geometry framework. The result is a non-perturbative theory of quantum
mechanics on (pseudo)-Riemannian manifolds. Within this approach, we derive
stochastic differential equations for massive spin-0 test particles charged
under scalar potentials, vector potentials and gravity. Furthermore, we derive
the associated Schr\"odinger equation. The resulting equations show that
massive scalar particles must be conformally coupled to gravity in a theory of
quantum gravity. We conclude with a discussion of some prospects of the
stochastic framework.


Abstract:
  A general form of codebook design for code-domain non-orthogonal multiple
access (CD-NOMA) can be considered equivalent to an autoencoder (AE)-based
constellation design for multi-user multidimensional modulation (MU-MDM). Due
to a constrained design space for optimal constellation, e.g., fixed resource
mapping and equal power allocation to all codebooks, however, existing AE
architectures produce constellations with suboptimal bit-error-rate (BER)
performance. Accordingly, we propose a new architecture for MU-MDM AE and
underlying training methodology for joint optimization of resource mapping and
a constellation design with bit-to-symbol mapping, aiming at approaching the
BER performance of a single-user MDM (SU-MDM) AE model with the same spectral
efficiency. The core design of the proposed AE architecture is dense resource
mapping combined with the novel power allocation layer that normalizes the sum
of user codebook power across the entire resources. This globalizes the domain
of the constellation design by enabling flexible resource mapping and power
allocation. Furthermore, it allows the AE-based training to approach a global
optimal MU-MDM constellations for CD-NOMA. Extensive BER simulation results
demonstrate that the proposed design outperforms the existing CD-NOMA designs
while approaching the single-user BER performance achieved by the equivalent
SU-MDM AE within 0.3 dB over the additive white Gaussian noise channel.


Abstract:
  We investigate the dynamics of wealth inequality in an economy where
households have positional preferences, with the strength of the positional
concern determined endogenously by inequality of wealth distribution in the
society. We demonstrate that in the long run such an economy converges to a
unique egalitarian steady-state equilibrium, with all households holding equal
positive wealth, when the initial inequality is sufficiently low. Otherwise,
the steady state is characterised by polarisation of households into rich, who
own all the wealth, and poor, whose wealth is zero. A fiscal policy with
government consumption funded by taxes on labour income and wealth can move the
economy from any initial state towards an egalitarian equilibrium with a higher
aggregate wealth.


Abstract:
  Drop condensation and evaportation as a result of the gradient in vapor
concentration are important in both engineering and natural systems. One of the
interesting natural examples is transpiration on plant leaves. Most of water in
the inner space of the leaves escapes through stomata, whose rate depends on
the surface topography and a difference in vapor concentrations inside and just
outside of the leaves. Previous research on the vapor flux on various surfaces
has focused on numerically solving the vapor diffusion equation or using
scaling arguments based on a simple solution with a flat surface. In this
present work, we present and discuss simple analytical solutions on various 2D
surface shapes (e.g., semicylinder, semi-ellipse, hair). The method of solving
the diffusion equation is to use the complex potential theory, which provides
analytical solutions for vapor concentration and flux. We find that a high mass
flux of vapor is formed near the top of the microstructures while a low mass
flux is developed near the stomata at the leaf surface. Such a low vapor flux
near the stomata may affect transpiration in two ways. First, condensed
droplets on the stomata will not grow due to a low mass flux of vapor, which
will not inhibit the gas exchange through the stomatal opening. Second, the low
mass flux from the atmosphere will facilitate the release of high concentrated
vapor from the substomatal space.


Abstract:
  We review methods to shuttle quantum particles fast and robustly. Ideal
robustness amounts to the invariance of the desired transport results with
respect to deviations, noisy or otherwise, from the nominal driving protocol
for the control parameters; this can include environmental perturbations.
"Fast" is defined with respect to adiabatic transport times. Special attention
is paid to shortcut-to-adiabaticity protocols that achieve, in
faster-than-adiabatic times, the same results of slow adiabatic driving.


Abstract:
  Similarity caching systems have recently attracted the attention of the
scientific community, as they can be profitably used in many application
contexts, like multimedia retrieval, advertising, object recognition,
recommender systems and online content-match applications. In such systems, a
user request for an object $o$, which is not in the cache, can be (partially)
satisfied by a similar stored object $o$', at the cost of a loss of user
utility. In this paper we make a first step into the novel area of similarity
caching networks, where requests can be forwarded along a path of caches to get
the best efficiency-accuracy tradeoff. The offline problem of content placement
can be easily shown to be NP-hard, while different polynomial algorithms can be
devised to approach the optimal solution in discrete cases. As the content
space grows large, we propose a continuous problem formulation whose solution
exhibits a simple structure in a class of tree topologies. We verify our
findings using synthetic and realistic request traces.


Abstract:
  The breadth of a Lie algebra $L$ is defined to be the maximal dimension of
the image of $ad_x=[x,-]:L\to L$, for $x\in L$. Here, we initiate an
investigation into the breadth of three families of Lie algebras defined by
posets and provide combinatorial breadth formulas for members of each family.


Abstract:
  Using a recently developed technique to estimate the equilibrium free energy
of glassy materials, we explore if equilibrium simulation methods can be used
to estimate the solubility of amorphous solids. As an illustration, we compute
the chemical potentials of the constituent particles of a two-component
Kob-Andersen model glass former. To compute the chemical potential for
different components, we combine the calculation of the overall free energy of
the glass with a calculation of the chemical potential difference of the two
components. We find that the standard method to compute chemical potential
differences by thermodynamic integration yields not only a wide scatter in the
chemical potential values but, more seriously, the average of the thermodynamic
integration results is well above the extrapolated value for the supercooled
liquid. However, we find that if we compute the difference of the chemical
potential of the components with the the non-equilibrium free energy expression
proposed by Jarzynski, we obtain a good match with the extrapolated value of
the supercooled liquid. The extension of the Jarzynski method that we propose
opens a potentially powerful route to compute free-energy related equilibrium
properties of glasses. We find that the solubility estimate of amorphous
materials obtained from direct coexistence simulations is only in fair
agreement with the solubility prediction based on the chemical potential
calculations of a hypothetical "well-equilibrated glass". In direct coexistence
simulations, we find that, in qualitative agreement with experiments, the
amorphous solubility decreases with time and attains a low solubility value.


Abstract:
  With the rapid development of wireless sensor networks, smart devices, and
traditional information and communication technologies, there is tremendous
growth in the use of Internet of Things (IoT) applications and services in our
everyday life. IoT systems deal with high volumes of data. This data can be
particularly sensitive, as it may include health, financial, location, and
other highly personal information. Fine-grained security management in IoT
demands effective access control. Several proposals discuss access control for
the IoT, however, a limited focus is given to the emerging blockchain-based
solutions for IoT access control. In this paper, we review the recent trends
and critical needs for blockchain-based solutions for IoT access control. We
identify several important aspects of blockchain, including decentralised
control, secure storage and sharing information in a trustless manner, for IoT
access control including their benefits and limitations. Finally, we note some
future research directions on how to converge blockchain in IoT access control
efficiently and effectively.


Abstract:
  Relative abundance is a common metric to estimate the composition of species
in ecological surveys reflecting patterns of commonness and rarity of
biological assemblages. Measurements of coral reef compositions formed by four
communities along Australia's Great Barrier Reef (GBR) gathered between 2012
and 2017 are the focus of this paper. We undertake the task of finding clusters
of transect locations with similar community composition and investigate
changes in clustering dynamics over time. During these years, an unprecedented
sequence of extreme weather events (cyclones and coral bleaching) impacted the
58 surveyed locations. The dependence between constituent parts of a
composition presents a challenge for existing multivariate clustering
approaches. In this paper, we introduce a finite mixture of Dirichlet
distributions with group-specific parameters, where cluster memberships are
dictated by unobserved latent variables. The inference is carried in a Bayesian
framework, where MCMC strategies are outlined to sample from the posterior
model. Simulation studies are presented to illustrate the performance of the
model in a controlled setting. The application of the model to the 2012 coral
reef data reveals that clusters were spatially distributed in similar ways
across reefs which indicates a potential influence of wave exposure at the
origin of coral reef community composition. The number of clusters estimated by
the model decreased from four in 2012 to two from 2014 until 2017. Posterior
probabilities of transect allocations to the same cluster substantially
increase through time showing a potential homogenization of community
composition across the whole GBR. The Bayesian model highlights the diversity
of coral reef community composition within a coral reef and rapid changes
across large spatial scales that may contribute to undermining the future of
the GBR's biodiversity.


Abstract:
  With the entrance of cosmology in its new era of high precision experiments,
low- and high-redshift observations set off tensions in the measurements of
both the present-day expansion rate ($H_0$) and the clustering of matter
($S_8$). We provide a simultaneous explanation of these tensions using the
Parker-Raval Vacuum Metamorphosis (VM) model with the neutrino sector extended
beyond the three massless Standard Model flavours and the curvature of the
universe considered as a model parameter. To estimate the effect on
cosmological observables we implement various extensions of the VM model in the
standard \texttt{CosmoMC} pipeline and establish which regions of parameter
space are empirically viable to resolve the $H_0$ and $S_8$ tensions. We find
that the likelihood analyses of the physically motivated VM model, which has
the same number of free parameters as in the spatially-flat $\Lambda$CDM model,
always gives $H_0$ in agreement with the local measurements (even when BAO or
Pantheon data are included) at the price of much larger $\chi^2$ than
$\Lambda$CDM. The inclusion of massive neutrinos and extra relativistic species
quantified through two well known parameters $\sum m_{\nu}$ and $N_{\rm eff}$,
does not modify this result, and in some cases improves the goodness of the
fit. In particular, for the original VM+$\sum m_\nu$+$N_{\rm eff}$ and the
Planck+BAO+Pantheon dataset combination, we find evidence for $\sum
m_{\nu}=0.80^{+0.18}_{-0.22}~{\rm eV}$ at more than $3\sigma$, no indication
for extra neutrino species, $H_0=71.0\pm1.2$~km/s/Mpc in agreement with local
measurements, and $S_8=0.755\pm0.032$ that solves the tension with the weak
lensing measurements. [Abridged]


Abstract:
  We compute the normalization of the multiple D-instanton amplitudes in type
IIB string theory and show that the result agrees with the prediction of
S-duality due to Green and Gutperle.


Abstract:
  In this paper, we prove that for suitably chosen Dwork motives, the local
Galois representation arising from middle cohomology of fibers over a point
with $p$-adic valuation $<0$ on the base is regular and ordinary. The result
will be crucial in the forthcoming work Potential Automorphy for $GL_n$. The
proof consists of the construction of a semistable blowup and a use of
Hyodo-Kato's log crystalline cohomology theory.


Abstract:
  State-of-the-art deep-learning-based voice activity detectors (VADs) are
often trained with anechoic data. However, real acoustic environments are
generally reverberant, which causes the performance to significantly
deteriorate. To mitigate this mismatch between training data and real data, we
simulate an augmented training set that contains nearly five million
utterances. This extension comprises of anechoic utterances and their
reverberant modifications, generated by convolutions of the anechoic utterances
with a variety of room impulse responses (RIRs). We consider five different
models to generate RIRs, and five different VADs that are trained with the
augmented training set. We test all trained systems in three different real
reverberant environments. Experimental results show $20\%$ increase on average
in accuracy, precision and recall for all detectors and response models,
compared to anechoic training. Furthermore, one of the RIR models consistently
yields better performance than the other models, for all the tested VADs.
Additionally, one of the VADs consistently outperformed the other VADs in all
experiments.


Abstract:
  This paper illustrates how multilevel functional models can detect and
characterize biomechanical changes along different sport training sessions. Our
analysis focuses on the relevant cases to identify differences in knee
biomechanics in recreational runners during low and high-intensity exercise
sessions with the same energy expenditure by recording $20$ steps. To do so, we
review the existing literature of multilevel models, and then, we propose a new
hypothesis test to look at the changes between different levels of the
multilevel model as low and high-intensity training sessions. We also evaluate
the reliability of measures recorded in three-dimension knee angles from the
functional intra-class correlation coefficient (ICC) obtained from the
decomposition performed with the multilevel funcional model taking into account
$20$ measures recorded in each test. The results show that there are no
statistically significant differences between the two modes of exercise.
However, we have to be careful with the conclusions since, as we have shown,
human gait-patterns are very individual and heterogeneous between groups of
athletes, and other alternatives to the p-value may be more appropriate to
detect statistical differences in biomechanical changes in this context.


Abstract:
  Re-identification (ReID) is to identify the same instance across different
cameras. Existing ReID methods mostly utilize alignment-based or
attention-based strategies to generate effective feature representations.
However, most of these methods only extract general feature by employing single
input image itself, overlooking the exploration of relevance between comparing
images. To fill this gap, we propose a novel end-to-end trainable dynamic
convolution framework named Instance and Pair-Aware Dynamic Networks in this
paper. The proposed model is composed of three main branches where a
self-guided dynamic branch is constructed to strengthen instance-specific
features, focusing on every single image. Furthermore, we also design a
mutual-guided dynamic branch to generate pair-aware features for each pair of
images to be compared. Extensive experiments are conducted in order to verify
the effectiveness of our proposed algorithm. We evaluate our algorithm in
several mainstream person and vehicle ReID datasets including CUHK03,
DukeMTMCreID, Market-1501, VeRi776 and VehicleID. In some datasets our
algorithm outperforms state-of-the-art methods and in others, our algorithm
achieves a comparable performance.


Abstract:
  We review the fundamentals and highlight the differences between some
commonly used definitions for the PPN gamma parameter ($\gamma$) and the
gravitational slip ($\eta$). Here we stress the usefulness of a gamma-like
parameter used by Berry and Gair ($\gamma_{\scriptscriptstyle \Sigma}$) that
parametrizes the bending of light and the Shapiro time delay in situations in
which the standard $\gamma$ cannot be promptly used. First we apply our
considerations to two well known cases, but for which some conflicting results
can be found: massive Brans-Dicke gravity and $f(R)$ gravity (both the metric
and the Palatini versions). Although the slip parameter is always well defined,
it has in general no direct relation to either light deflection or the Shapiro
time delay, hence care should be taken on imposing the PPN $\gamma$ bounds on
the slip. We stress that, for any system with a well posed Newtonian limit,
Palatini $f(R)$ theories always have $\gamma = 1$; while metric $f(R)$ theories
can only have two values: either 1 or 1/2. The extension towards Horndeski
gravity shows no qualitative surprises, and $\gamma_{\scriptscriptstyle
\Sigma}$ is a constant in this context (only assuming that the Horndeski
potentials can be approximated by analytical functions). This implies that a
precise study on the bending of light for different impact parameters can in
principle be used to rule out the complete Horndeski action as an action for
gravity. Also, we comment on the consequences for $\gamma$ inferences at
external galaxies.


Abstract:
  We study fair resource allocation under a connectedness constraint wherein a
set of indivisible items are arranged on a path and only connected subsets of
items may be allocated to the agents. An allocation is deemed fair if it
satisfies equitability up to one good (EQ1), which requires that agents'
utilities are approximately equal. We show that achieving EQ1 in conjunction
with well-studied measures of economic efficiency (such as Pareto optimality,
non-wastefulness, maximum egalitarian or utilitarian welfare) is
computationally hard even for binary additive valuations. On the algorithmic
side, we show that by relaxing the efficiency requirement, a connected EQ1
allocation can be computed in polynomial time for any given ordering of agents,
even for general monotone valuations. Interestingly, the allocation computed by
our algorithm has the highest egalitarian welfare among all allocations
consistent with the given ordering. On the other hand, if efficiency is
required, then tractability can still be achieved for binary additive
valuations with interval structure. On our way, we strengthen some of the
existing results in the literature for other fairness notions such as
envy-freeness up to one good (EF1), and also provide novel results for
negatively-valued items or chores.


Abstract:
  We derive a simple and precise approximation to probability density functions
in sampling distributions based on the Fourier cosine series. After clarifying
the required conditions, we illustrate the approximation on two examples: the
distribution of the sum of uniformly distributed random variables, and the
distribution of sample skewness drawn from a normal population. The probability
density function of the first example can be explicitly expressed, but that of
the second example has no explicit expression.


Abstract:
  We analytically compute all four-loop QCD corrections to the photon-quark and
Higgs-gluon form factors involving a closed massless fermion loop. Our
calculation of non-planar vertex integrals confirms a previous conjecture for
the analytical form of the non-fermionic contributions to the collinear
anomalous dimensions of quarks and gluons.


Abstract:
  $^{72}$As is a promising positron emitter for diagnostic imaging that can be
employed locally using a $^{72}$Se generator. However, current reaction
pathways to $^{72}$Se have insufficient nuclear data for efficient production
using regional 100-200 MeV high-intensity proton accelerators. In order to
address this deficiency, stacked-target irradiations were performed at LBNL,
LANL, and BNL to measure the production of the $^{72}$Se/$^{72}$As PET
generator system via $^{75}$As(p,x) between 35 and 200 MeV. This work provides
the most well-characterized excitation function for $^{75}$As(p,4n)$^{72}$Se
starting from threshold. Additional focus was given to report the first
measurements of $^{75}$As(p,x)$^{68}$Ge and bolster an already robust
production capability for the highly valuable $^{68}$Ge/$^{68}$Ga PET
generator. Thick target yield comparisons with prior established formation
routes to both generators are made. In total, high-energy proton-induced cross
sections are reported for 55 measured residual products from $^{75}$As, Cu, and
Ti targets, where the latter two materials were present as monitor foils. These
results were compared with literature data as well as the default theoretical
calculations of the nuclear model codes TALYS, CoH, EMPIRE, and ALICE. Reaction
modeling at these energies is typically unsatisfactory due to few prior
published data and many interacting physics models. Therefore, a detailed
assessment of the TALYS code was performed with simultaneous parameter
adjustments applied according to a standardized procedure. Particular attention
was paid to the formulation of the two-component exciton model in the
transition between the compound and pre-equilibrium regions, with a linked
investigation of level density models for nuclei off of stability and their
impact on modeling predictive power.


Abstract:
  In general, reliable communication via multiple-input multiple-output (MIMO)
orthogonal frequency division multiplexing (OFDM) requires accurate channel
estimation at the receiver. The existing literature largely focuses on
denoising methods for channel estimation that depend on either (i)~channel
analysis in the time-domain with prior channel knowledge or (ii)~supervised
learning techniques which require large pre-labeled datasets for training. To
address these limitations, we present a frequency-domain denoising method based
on a reinforcement learning framework that does not need a priori channel
knowledge and pre-labeled data. Our methodology includes a new successive
channel denoising process based on channel curvature computation, for which we
obtain a channel curvature magnitude threshold to identify unreliable channel
estimates. Based on this process, we formulate the denoising mechanism as a
Markov decision process, where we define the actions through a geometry-based
channel estimation update, and the reward function based on a policy that
reduces mean squared error (MSE). We then resort to Q-learning to update the
channel estimates. Numerical results verify that our denoising algorithm can
successfully mitigate noise in channel estimates. In particular, our algorithm
provides a significant improvement over the practical least squares (LS)
estimation method and provides performance that approaches that of the ideal
linear minimum mean square error (LMMSE) estimation with perfect knowledge of
channel statistics.


Abstract:
  Tactile perception using vibration sensation helps robots recognize their
environment's physical properties and perform complex tasks. A sliding motion
is applied to target objects to generate tactile vibration data. However,
situations exist where such a sliding motion is infeasible due to geometrical
constraints in the environment or an object's fragility which cannot resist
friction forces. This paper explores a novel approach to achieve
vibration-based tactile perception without a sliding motion. To this end, our
key idea is injecting a mechanical vibration into a soft tactile sensor system
and measuring the propagated vibration inside it by a sensor. Soft tactile
sensors are deformed by the contact state, and the touched objects' shape or
texture should change the characteristics of the vibration propagation.
Therefore, the propagated-vibration data are expected to contain useful
information for recognizing touched environments. We developed a prototype
system for a proof-of-concept: a mechanical vibration is applied to a
biomimetic (soft and vibration-based) tactile sensor from a small, mounted
piezoelectric actuator. As a verification experiment, we performed two
classification tasks for sandpaper's grit size and a slit's gap widths using
our approach and compared their accuracies with that of using sliding motions.
Our approach resulted in 70% accuracy for the grit size classification and 99%
accuracy for the gap width classification. These results are comparable to or
better than the comparison methods with a sliding motion.


Abstract:
  Biological synaptic plasticity exhibits nonlinearities that are not accounted
for by classic Hebbian learning rules. Here, we introduce a simple family of
generalized nonlinear Hebbian learning rules. We study the computations
implemented by their dynamics in the simple setting of a neuron receiving
feedforward inputs. These nonlinear Hebbian rules allow a neuron to learn
tensor decompositions of its higher-order input correlations. The particular
input correlation decomposed and the form of the decomposition depend on the
location of nonlinearities in the plasticity rule. For simple, biologically
motivated parameters, the neuron learns eigenvectors of higher-order input
correlation tensors. We prove that tensor eigenvectors are attractors and
determine their basins of attraction. We calculate the volume of those basins,
showing that the dominant eigenvector has the largest basin of attraction. We
then study arbitrary learning rules and find that any learning rule that admits
a finite Taylor expansion into the neural input and output also has stable
equilibria at generalized eigenvectors of higher-order input correlation
tensors. Nonlinearities in synaptic plasticity thus allow a neuron to encode
higher-order input correlations in a simple fashion.


Abstract:
  We consider reversible and surjective cellular automata perturbed with noise.
We show that, in the presence of positive additive noise, the cellular
automaton forgets all the information regarding its initial configuration
exponentially fast. In particular, the state of a finite collection of cells
with diameter n becomes indistinguishable from pure noise after O(log n) time
steps. This highlights the seemingly unavoidable need for irreversibility in
order to perform scalable reliable computation in the presence of noise.


Abstract:
  Automatic classification of disordered speech can provide an objective tool
for identifying the presence and severity of speech impairment. Classification
approaches can also help identify hard-to-recognize speech samples to teach ASR
systems about the variable manifestations of impaired speech. Here, we develop
and compare different deep learning techniques to classify the intelligibility
of disordered speech on selected phrases. We collected samples from a diverse
set of 661 speakers with a variety of self-reported disorders speaking 29 words
or phrases, which were rated by speech-language pathologists for their overall
intelligibility using a five-point Likert scale. We then evaluated classifiers
developed using 3 approaches: (1) a convolutional neural network (CNN) trained
for the task, (2) classifiers trained on non-semantic speech representations
from CNNs that used an unsupervised objective [1], and (3) classifiers trained
on the acoustic (encoder) embeddings from an ASR system trained on typical
speech [2]. We found that the ASR encoder's embeddings considerably outperform
the other two on detecting and classifying disordered speech. Further analysis
shows that the ASR embeddings cluster speech by the spoken phrase, while the
non-semantic embeddings cluster speech by speaker. Also, longer phrases are
more indicative of intelligibility deficits than single words.


Abstract:
  Motivated by the rising abundance of observational data with continuous
treatments, we investigate the problem of estimating the average dose-response
curve (ADRF). Available parametric methods are limited in their model space,
and previous attempts in leveraging neural network to enhance model
expressiveness relied on partitioning continuous treatment into blocks and
using separate heads for each block; this however produces in practice
discontinuous ADRFs. Therefore, the question of how to adapt the structure and
training of neural network to estimate ADRFs remains open. This paper makes two
important contributions. First, we propose a novel varying coefficient neural
network (VCNet) that improves model expressiveness while preserving continuity
of the estimated ADRF. Second, to improve finite sample performance, we
generalize targeted regularization to obtain a doubly robust estimator of the
whole ADRF curve.