Abstract:
  We present a theoretical method for deriving the stress tensor and elastic
response of ordered systems within a Ginzburg-Landau type density field theory
in the linear regime. This is based on spatially coarse graining the
microscopic stress which is determined by the variation of a free energy with
respect to mass displacements. We find simple expressions for the stress tensor
for phase field crystal (PFC) models for different crystal symmetries in two
and three dimensions. Using tetradic product sums of reciprocal lattice
vectors, we calculate elastic constants and show that they are directly related
to the symmetries of the reciprocal lattices. We also show that except for bcc
lattices, there are regions of model parameters for which the elastic response
is isotropic. The predicted elastic stress-strain curves are verified by
numerical strain-controlled bulk and shear deformations. Since the method is
independent of a reference state, it extends also to defected crystals. We
exemplify this by considering an edge and screw dislocation in the simple cubic
lattice.


Abstract:
  Recent advances in statistics introduced versions of the central limit
theorem for high-dimensional vectors, allowing for the construction of
confidence regions for high-dimensional parameters. In this note, $s$-sparsely
convex high-dimensional confidence regions are compared with respect to their
volume. Specific confidence regions which are based on $\ell_p$-balls are found
to have exponentially smaller volume than the corresponding hypercube. The
theoretical results are validated by a comprehensive simulation study.


Abstract:
  A quasiconformal tree is a doubling metric tree in which the diameter of each
arc is bounded above by a fixed multiple of the distance between its endpoints.
In this paper we show that every quasiconformal tree bi-Lipschitz embeds in
some Euclidean space, with the ambient dimension and the bi-Lipschitz constant
depending only on the doubling and bounded turning constants of the tree. This
answers Question 1.6 in \cite{DV} (arXiv:2007.12297).


Abstract:
  Three-loop counterterms for the Standard Model (SM) revealed that the matrix
of anomalous dimensions ($\gamma$) of quarks is divergent in the $d \to 4$
limit unless a carefully chosen non-Hermitian square-root of $Z$ matrix is used
in the textbook formula for $\gamma$. Here, an alternative prescription is
given, which expresses $\gamma$ and $\beta$ functions directly in terms of
counterterms (instead of $\sqrt{Z}$ and conventional `bare couplings') and
produces finite results. In the SM, this prescription $automatically$
reproduces results obtained previously by adjusting $\sqrt{Z}$.


Abstract:
  The PrandtlPlane aircraft has been recently considered as a possible
candidate to foster the ambition of a greener aviation. Despite the relevant
amount of research carried out in the last years, several aspects of this novel
configuration still need further insight to pave the way to future
applications. Among them, the coupled flight-dynamic and aeroelastic response
is addressed in this work by means of a dedicated in-house framework. For the
evaluation of the aerodynamic forces, an enhanced Doublet Lattice Method, able
to take into account terms typically neglected by classic formulations, is
employed. First, flight-dynamic aspects are considered, showing how effects of
interaction between Short Period or Dutch Roll with elastic modes remarkably
deteriorate the flying qualities. Then, focus is on the aeroelastic stability
of the aircraft. As observed also in previous literature efforts on this
configuration, flutter onset is considerably different when considering the
aircraft being free in the air or fixed in space. Thanks to the adopted
formulation it is shown how, for this PrandtlPlane, the aerodynamic coupling of
elastic and rigid modes has a beneficial effect on flutter onset. However, the
different modal properties, consequence of the diverse boundary conditions,
when switching from fixed-in-space to free-flying aircraft, also play a
relevant role in determining the occurrence of flutter. Whereas for the
longitudinal case both effects contribute increasing flutter speed, for the
lateral-directional case the variation in modal properties has a detrimental
and dominating effect, leading to a flutter speedwell within the flight
envelope.Finally,the work discusses the contributions of the additional terms
modeled by the enhanced Doublet Lattice Method, showing how they induce a
considerable effect when modeling the flight dynamics of the flexible aircraft.


Abstract:
  In paper by I.T. Habibullin and our joint paper the algorithm for
classification of integrable equations with three independent variables was
proposed. This method is based on the requirement of the existence of an
infinite set of Darboux integrable reductions and on the notion of the
characteristic Lie-Rinehart algebras. The method was applied for the
classification of integrable cases of different subclasses of equations
$u_{n,xy} = f(u_{n+1},u_n,u_{n-1}, u_{n,x},u_{n,y})$ of special forms. Under
this approach the novel integrable chain was obtained. In present paper we
construct Lax pair for the novel chain. To construct the Lax pair, we use the
scheme suggested in papers by E.V. Ferapontov. We also study the periodic
reduction of the chain.


Abstract:
  Attitude estimation is the process of computing the orientation angles of an
object with respect to a fixed frame of reference. Gyroscope, accelerometer,
and magnetometer are some of the fundamental sensors used in attitude
estimation. The orientation angles computed from these sensors are combined
using the sensor fusion methodologies to obtain accurate estimates. The
complementary filter is one of the widely adopted techniques whose performance
is highly dependent on the appropriate selection of its gain parameters. This
paper presents a novel cascaded architecture of the complementary filter that
employs a nonlinear and linear version of the complementary filter within one
framework. The nonlinear version is used to correct the gyroscope bias, while
the linear version estimates the attitude angle. The significant advantage of
the proposed architecture is its independence of the filter parameters, thereby
avoiding tuning the filters gain parameters. The proposed architecture does not
require any mathematical modeling of the system and is computationally
inexpensive. The proposed methodology is applied to the real-world datasets,
and the estimation results were found to be promising compared to the other
state-of-the-art algorithms.


Abstract:
  We conducted a survey of 67 graduate students enrolled in the Privacy and
Security in Healthcare course at Indiana University Purdue University
Indianapolis. This was done to measure user preference and their understanding
of usability and security of three different Electronic Health Records
authentication methods: single authentication method (username and password),
Single sign-on with Central Authentication Service (CAS) authentication method,
and a bio-capsule facial authentication method. This research aims to explore
the relationship between security and usability, and measure the effect of
perceived security on usability in these three aforementioned authentication
methods. We developed a formative-formative Partial Least Square Structural
Equation Modeling (PLS-SEM) model to measure the relationship between the
latent variables of Usability, and Security. The measurement model was
developed using five observed variables (measures). - Efficiency and
Effectiveness, Satisfaction, Preference, Concerns, and Confidence. The results
obtained highlight the importance and impact of these measures on the latent
variables and the relationship among the latent variables. From the PLS-SEM
analysis, it was found that security has a positive impact on usability for
Single sign-on and bio-capsule facial authentication methods. We conclude that
the facial authentication method was the most secure and usable among the three
authentication methods. Further, descriptive analysis was done to draw out the
interesting findings from the survey regarding the observed variables.


Abstract:
  Scientific CMOS image sensors are a modern alternative for a typical CCD
detectors, as they offer both low read-out noise, large sensitive area, and
high frame rates. All these makes them promising devices for a modern
wide-field sky surveys. However, the peculiarities of CMOS technology have to
be properly taken into account when analyzing the data. In order to
characterize these, we performed an extensive laboratory testing of two Andor
cameras based on sCMOS chips -- Andor Neo and Andor Marana. Here we report its
results, especially on the temporal stability, linearity and image persistence.
We also present the results of an on-sky testing of these sensors connected to
a wide-field lenses, and discuss its applications for an astronomical sky
surveys.


Abstract:
  In this paper, we study the existence of minimizers of the Sobolev quotient
for a class of nonlocal operators with an orthotropic structure having
different exponents of integrability and different orders of differentiability.
Our method is based on the concentration-compactness principle which we extend
to this class of operators. One consequence of our main result is the existence
of a nontrivial nonnegative solution to the corresponding critical problem.


Abstract:
  With the successes of $f(R)$ theory as a neutral modification of Einstein's
general relativity (GR), we continue our study in this field and attempt to
find general %natural { neutral} and charged black hole (BH) solutions. In the
previous papers \cite{Nashed:2020mnp,Nashed:2020tbp}, we applied the field
equation of the $f(R)$ gravity to a spherically symmetric space-time
$ds^2=-U(r)dt^2+\frac{dr^2}{V(r)}+r^2 \left( d\theta^2+\sin^2\theta d\phi^2
\right)$ with unequal metric potentials $U(r)$ and $V(r)$ and with/without
electric charge. {Then we have obtained equations which include all the
possible static solutions with spherical symmetry.} To ensure the closed form
of system of the resulting differential equations in order to obtain specific
solutions, we assumed the derivative of the $f(R)$ with respect to the scalar
curvature $R$ to have a form %$F_1(r)=\frac{df(R(r))}{dR(r)} \propto
%{\color{red} 1 +} %\frac{c}{r^n}$ but in case $n>2$, the resulting black hole
solutions with/without charge do not %generate asymptotically GR BH solutions
in the limit $c\rightarrow 0$ which means that the only case that can generate
GR BHs is $n=2$. %In this paper, we assume another form, i.e., $F_1(r)
{=\frac{df(R(r))}{dR(r)} } = 1-\frac{F_0-\left(n-3\right)}{r^n}$ with a
constant $F_0$ and show that we can generate asymptotically GR BH solutions for
$n>2$ but we show that the $n=2$ case is not allowed. This form of $F_1(r)$
could be the most acceptable physical form that we can generate from it
physical metric potentials that can have a well-known asymptotic form and we
obtain the metric of the Einstein general relativity in the limit of $F_0\to
n-3$. We show that the form of the electric charge depends on $n$ and that
$n\neq 2$.}


Abstract:
  Online algorithms for detecting changepoints, or abrupt shifts in the
behavior of a time series, are often deployed with limited resources, e.g., to
edge computing settings such as mobile phones or industrial sensors. In these
scenarios it may be beneficial to trade the cost of collecting an environmental
measurement against the quality or "fidelity" of this measurement and how the
measurement affects changepoint estimation. For instance, one might decide
between inertial measurements or GPS to determine changepoints for motion. A
Bayesian approach to changepoint detection is particularly appealing because we
can represent our posterior uncertainty about changepoints and make active,
cost-sensitive decisions about data fidelity to reduce this posterior
uncertainty. Moreover, the total cost could be dramatically lowered through
active fidelity switching, while remaining robust to changes in data
distribution. We propose a multi-fidelity approach that makes cost-sensitive
decisions about which data fidelity to collect based on maximizing information
gain with respect to changepoints. We evaluate this framework on synthetic,
video, and audio data and show that this information-based approach results in
accurate predictions while reducing total cost.


Abstract:
  A Q-system in a C* 2-category is a unitary version of a separable Frobenius
algebra object and can be viewed as a unitary version of a higher idempotent.
We define a higher unitary idempotent completion for C* 2-categories called
Q-system completion and study its properties. We show that the C* 2-category of
right correspondences of unital C*-algebras is Q-system complete by
constructing an inverse realization $\dag$ 2-functor. We use this result to
construct induced actions of group theoretical unitary fusion categories on
continuous trace C*-algebras with connected spectra.


Abstract:
  Network science is the field dedicated to the investigation and analysis of
complex systems via their representations as networks. We normally model such
networks as graphs: sets of nodes connected by sets of edges and a number of
node and edge attributes. This deceptively simple object is the starting point
of never-ending complexity, due to its ability to represent almost every facet
of reality: chemical interactions, protein pathways inside cells, neural
connections inside the brain, scientific collaborations, financial relations,
citations in art history, just to name a few examples. If we hope to make sense
of complex networks, we need to master a large analytic toolbox: graph and
probability theory, linear algebra, statistical physics, machine learning,
combinatorics, and more.
  This book aims at providing the first access to all these tools. It is
intended as an "Atlas", because its interest is not in making you a specialist
in using any of these techniques. Rather, after reading this book, you will
have a general understanding about the existence and the mechanics of all these
approaches. You can use such an understanding as the starting point of your own
career in the field of network science. This has been, so far, an
interdisciplinary endeavor. The founding fathers of this field come from many
different backgrounds: mathematics, sociology, computer science, physics,
history, digital humanities, and more. This Atlas is charting your path to be
something different from all of that: a pure network scientist.


Abstract:
  We investigate the noise sensitivity of the top eigenvector of a sparse
random symmetric matrix. Let $v$ be the top eigenvector of an $N\times N$
sparse random symmetric matrix with an average of $d$ non-zero centered entries
per row. We resample $k$ randomly chosen entries of the matrix and obtain
another realization of the random matrix with top eigenvector $v^{[k]}$.
Building on recent results on sparse random matrices and a noise sensitivity
analysis previously developed for Wigner matrices, we prove that, if $d\geq
N^{2/9}$, with high probability, when $k \ll N^{5/3}$, the vectors $v$ and
$v^{[k]}$ are almost collinear and, on the contrary, when $k\gg N^{5/3}$, the
vectors $v$ and $v^{[k]}$ are almost orthogonal. A similar result holds for the
eigenvector associated to the second largest eigenvalue of the adjacency matrix
of an Erd\H{o}s-R\'enyi random graph with average degree $d \geq N^{2/9}$.


Abstract:
  Faraday complexity describes whether a spectropolarimetric observation has
simple or complex magnetic structure. Quickly determining the Faraday
complexity of a spectropolarimetric observation is important for processing
large, polarised radio surveys. Finding simple sources lets us build rotation
measure grids, and finding complex sources lets us follow these sources up with
slower analysis techniques or further observations. We introduce five features
that can be used to train simple, interpretable machine learning classifiers
for estimating Faraday complexity. We train logistic regression and extreme
gradient boosted tree classifiers on simulated polarised spectra using our
features, analyse their behaviour, and demonstrate our features are effective
for both simulated and real data. This is the first application of machine
learning methods to real spectropolarimetry data. With 95 per cent accuracy on
simulated ASKAP data and 90 per cent accuracy on simulated ATCA data, our
method performs comparably to state-of-the-art convolutional neural networks
while being simpler and easier to interpret. Logistic regression trained with
our features behaves sensibly on real data and its outputs are useful for
sorting polarised sources by apparent Faraday complexity.


Abstract:
  In this paper, the one-dimensional compressible Navier-Stokes system with
outer pressure boundary conditions is investigated. Under some suitable
assumptions, we prove that the specific volume and the temperature are bounded
from below and above independently of time, and then give the local and global
existence of strong solutions. Furthermore, we also obtain the convergence of
the global strong solution to a stationary state and the nonlinearly stability
of its convergence. It is worth noticing that all the assumptions imposed on
the initial data are the same as Takeyuki Nagasawa [Japan.J.Appl.Math.(1988)].
Therefore, our work can be regarded as an improvement of the results of
Takeyuki.


Abstract:
  Linear-time algorithms that are traditionally used to shuffle data on CPUs,
such as the method of Fisher-Yates, are not well suited to implementation on
GPUs due to inherent sequential dependencies. Moreover, existing parallel
shuffling algorithms show unsatisfactory performance on GPU architectures
because they incur a large number of read/write operations to high latency
global memory. To address this, we provide a method of generating pseudo-random
permutations in parallel by fusing suitable pseudo-random bijective functions
with stream compaction operations. Our algorithm, termed `bijective shuffle'
trades increased per-thread arithmetic operations for reduced global memory
transactions. It is work-efficient, deterministic, and only requires a single
global memory read and write per shuffle input, thus maximising use of global
memory bandwidth. To empirically demonstrate the correctness of the algorithm,
we develop a consistent, linear time, statistical test for the quality of
pseudo-random permutations based on kernel space embeddings. Empirical results
show that the bijective shuffle algorithm outperforms competing algorithms on
multicore CPUs and GPUs, showing improvements of between one and two orders of
magnitude and approaching peak device bandwidth.


Abstract:
  OBJECTIVE: Leverage existing biomedical NLP tools and DS domain terminology
to produce a novel and comprehensive knowledge graph containing dietary
supplement (DS) information for discovering interactions between DS and drugs,
or Drug-Supplement Interactions (DSI). MATERIALS AND METHODS: We created
SemRepDS (an extension of SemRep), capable of extracting semantic relations
from abstracts by leveraging a DS-specific terminology (iDISK) containing
28,884 DS terms not found in the UMLS. PubMed abstracts were processed using
SemRepDS to generate semantic relations, which were then filtered using a
PubMedBERT-based model to remove incorrect relations before generating our
knowledge graph (SuppKG). Two pathways are used to identify potential DS-Drug
interactions which are then evaluated by medical professionals for mechanistic
plausibility. RESULTS: Comparison analysis found that SemRepDS returned 206.9%
more DS relations and 158.5% more DS entities than SemRep. The fine-tuned BERT
model obtained an F1 score of 0.8605 and removed 43.86% of the relations,
improving the precision of the relations by 26.4% compared to pre-filtering.
SuppKG consists of 2,928 DS-specific nodes. Manual review of findings
identified 44 (88%) proposed DS-Gene-Drug and 32 (64%) proposed
DS-Gene1-Function-Gene2-Drug pathways to be mechanistically plausible.
DISCUSSION: The additional relations extracted using SemRepDS generated SuppKG
that was used to find plausible DSI not found in the current literature. By the
nature of the SuppKG, these interactions are unlikely to have been found using
SemRep without the expanded DS terminology. CONCLUSION: We successfully extend
SemRep to include DS information and produce SuppKG which can be used to find
potential DS-Drug interactions.


Abstract:
  Data scientists often collaborate with clients to analyze data to meet a
client's needs. What does the end-to-end workflow of a data scientist's
collaboration with clients look like throughout the lifetime of a project? To
investigate this question, we interviewed ten data scientists (5 female, 4
male, 1 non-binary) in diverse roles across industry and academia. We
discovered that they work with clients in a six-stage outer-loop workflow,
which involves 1) laying groundwork by building trust before a project begins,
2) orienting to the constraints of the client's environment, 3) collaboratively
framing the problem, 4) bridging the gap between data science and domain
expertise, 5) the inner loop of technical data analysis work, 6) counseling to
help clients emotionally cope with analysis results. This novel outer-loop
workflow contributes to CSCW by expanding the notion of what collaboration
means in data science beyond the widely-known inner-loop technical workflow
stages of acquiring, cleaning, analyzing, modeling, and visualizing data. We
conclude by discussing the implications of our findings for data science
education, parallels to design work, and unmet needs for tool development.


Abstract:
  It is demonstrated theoretically that the circularly polarized irradiation of
two-dimensional conducting systems can produce the composite bosons consisting
of two electrons with different effective masses (different charge carriers),
which are stable due to the Fermi sea of conduction electrons. As a result, the
optically induced mixture of paired electrons and normal conduction electrons
(the hybrid Bose-Fermi system) appears. Elementary excitations in such a hybrid
system are analyzed and possible manifestations of the light-induced electron
pairing are discussed for semiconductor quantum wells.


Abstract:
  This paper focuses on the modulation instability, conservation laws and
localized wave solutions of the generalized coupled Fokas-Lenells equation.
Based on the theory of linear stability analysis, distribution pattern of
modulation instability gain G in the (K,k) frequency plane is depicted, and the
constraints for the existence of rogue waves are derived. Subsequently, we
construct the infinitely many conservation laws for the generalized coupled
Fokas-Lenells equation from the Riccati-type formulas of the Lax pair. In
addition, the compact determinant expressions of the N-order localized wave
solutions are given via generalized Darboux transformation, including
higher-order rogue waves and interaction solutions among rogue waves with
bright-dark solitons or breathers. These solutions are parameter controllable:
(m_i,n_i) and (\alpha,\beta) control the structure and ridge deflection of
solution respectively, while the value of |d| controls the strength of
interaction to realize energy exchange. Especially, when d=0, the interaction
solutions degenerate into the corresponding order of rogue waves.


Abstract:
  Tensor algebra finds applications in various domains, and these applications,
especially when accelerated on spatial hardware accelerators, can deliver high
performance and low power. Spatial hardware accelerator exhibits complex design
space. Prior approaches based on manual implementation lead to low programming
productivity, rendering thorough design space exploration impossible. In this
paper, we propose TensorLib, a framework for generating spatial hardware
accelerator for tensor algebra applications. TensorLib is motivated by the
observation that, different dataflows share common hardware modules, which can
be reused across different designs. To build such a framework, TensorLib first
uses Space-Time Transformation to explore different dataflows, which can
compactly represent the hardware dataflow using a simple transformation matrix.
Next, we identify the common structures of different dataflows and build
parameterized hardware module templates with Chisel. Our generation framework
can select the needed hardware modules for each dataflow, connect the modules
using a specified interconnection pattern, and automatically generate the
complete hardware accelerator design. TensorLib remarkably improves the
productivity for the development and optimization of spatial hardware
architecture, providing a rich design space with trade-offs in performance,
area, and power. Experiments show that TensorLib can automatically generate
hardware designs with different dataflows and achieve 21\% performance
improvement on FPGA compared to the state-of-the-arts.


Abstract:
  In this paper we demonstrate that valley-ridge inflection (VRI) points of a
potential energy surface (PES) have a dynamical influence on the fate of
trajectories of the underlying Hamiltonian system. These points have attracted
the attention of chemists in the past decades when studying selectivity
problems in organic chemical reactions whose energy landscape exhibits a
post-transition-state bifurcation in the region between two sequential saddles
without an intervening energy minimum. To address the dynamical significance of
valley-ridge inflection points, we construct a symmetric potential energy
function that allows us to move the location of the VRI point while keeping the
locations and energies of the critical points fixed. In this setup, we carry
out a parametric study of the dynamical behavior of ensembles of trajectories
in terms of the energy of the chemical system and the position of the VRI
point. Our analysis reveals that the location of the VRI point controls the
fraction of trajectories that recross the high energy saddle region of the PES
without entering either of the potential wells that are separated by the low
energy saddle.


Abstract:
  Measuring biases of vision systems with respect to protected attributes like
gender and age is critical as these systems gain widespread use in society.
However, significant correlations between attributes in benchmark datasets make
it difficult to separate algorithmic bias from dataset bias. To mitigate such
attribute confounding during bias analysis, we propose a matching approach that
selects a subset of images from the full dataset with balanced attribute
distributions across protected attributes. Our matching approach first projects
real images onto a generative adversarial network (GAN)'s latent space in a
manner that preserves semantic attributes. It then finds image matches in this
latent space across a chosen protected attribute, yielding a dataset where
semantic and perceptual attributes are balanced across the protected attribute.
We validate projection and matching strategies with qualitative, quantitative,
and human annotation experiments. We demonstrate our work in the context of
gender bias in multiple open-source facial-recognition classifiers and find
that bias persists after removing key confounders via matching. Code and
documentation to reproduce the results here and apply the methods to new data
is available at https://github.com/csinva/matching-with-gans .


Abstract:
  We revisit the corrections to black holes due to the $R^4$ terms in the
action. We discuss corrections to the metric and possible scalar fields, as
well as corrections to thermodynamic quantities. We also comment on the large
$D$ limit of the solutions.


Abstract:
  In this article, we prove a toroidalization principle for finite actions on
klt singularities. As an application, we prove that the Jordan property for the
regional fundamental group of klt singularities can be realized geometrically:
by extracting a toric singularity over the klt germ. In the course of the
proof, we will prove statements about finite actions on dual complexes and
almost fixed points in the fibers of equivariant Fano type morphisms.
Furthermore, we will prove that the rank of a fundamental group of the klt
singularity is bounded above by its regularity.


Abstract:
  Semi-supervised (SS) inference has received much attention in recent years.
Apart from a moderate-sized labeled data, L, the SS setting is characterized by
an additional, much larger sized, unlabeled data, U. The setting of |U| >> |L|,
makes SS inference unique and different from the standard missing data
problems, owing to natural violation of the so-called 'positivity' or 'overlap'
assumption. However, most of the SS literature implicitly assumes L and U to be
equally distributed, i.e., no selection bias in the labeling. Inferential
challenges in missing at random (MAR) type labeling allowing for selection
bias, are inevitably exacerbated by the decaying nature of the propensity score
(PS). We address this gap for a prototype problem, the estimation of the
response's mean. We propose a double robust SS (DRSS) mean estimator and give a
complete characterization of its asymptotic properties. The proposed estimator
is consistent as long as either the outcome or the PS model is correctly
specified. When both models are correctly specified, we provide inference
results with a non-standard consistency rate that depends on the smaller size
|L|. The results are also extended to causal inference with imbalanced
treatment groups. Further, we provide several novel choices of models and
estimators of the decaying PS, including a novel offset logistic model and a
stratified labeling model. We present their properties under both high and low
dimensional settings. These may be of independent interest. Lastly, we present
extensive simulations and also a real data application.


Abstract:
  Many theoretical studies of the voter model (or variations thereupon) involve
order parameters that are population-averaged. While enlightening, such
quantities may obscure important statistical features that are only apparent on
the level of the individual. In this work, we ask which factors contribute to a
single voter maintaining a long-term statistical bias for one opinion over the
other in the face of social influence. To this end, a modified version of the
network voter model is proposed, which also incorporates quenched disorder in
the interaction strengths between individuals and the possibility of
antagonistic relationships. We find that a sparse interaction network and
heterogeneity in interaction strengths give rise to the possibility of
arbitrarily long-lived individual biases, even when there is no
population-averaged bias for one opinion over the other. This is demonstrated
by calculating the eigenvalue spectrum of the weighted network Laplacian using
the theory of sparse random matrices.


Abstract:
  We establish transportation cost inequalities (TCI) with respect to the
quantum Wasserstein distance by introducing quantum extensions of well-known
classical methods: first, using a non-commutative version of Ollivier's coarse
Ricci curvature, we prove that high temperature Gibbs states of commuting
Hamiltonians on arbitrary hypergraphs $H=(V,E)$ satisfy a TCI with constant
scaling as $O(|V|)$. Second, we argue that the temperature range for which the
TCI holds can be enlarged by relating it to recently established modified
logarithmic Sobolev inequalities. Third, we prove that the inequality still
holds for fixed points of arbitrary reversible local quantum Markov semigroups
on regular lattices, albeit with slightly worsened constants, under a seemingly
weaker condition of local indistinguishability of the fixed points. Finally, we
use our framework to prove Gaussian concentration bounds for the distribution
of eigenvalues of quasi-local observables and argue the usefulness of the TCI
in proving the equivalence of the canonical and microcanonical ensembles and an
exponential improvement over the weak Eigenstate Thermalization Hypothesis.


Abstract:
  Phenotypic noise underpins homeostasis and fitness of individual cells. Yet,
the extent to which noise shapes cell-to-population properties in microbial
active matter remains poorly understood. By quantifying variability in
confluent \textit{E.coli} strains, we catalogue noise across different
phenotypic traits. The noise, measured over different temperatures serving as
proxy for cellular activity, spanned more than two orders of magnitude. The
maximum noise was associated with the cell geometry and the critical colony
area at the onset of mono-to-multilayer transition (MTMT), while the lower
bound was set by the critical time of the MTMT. Our results, supported by a
hydrodynamic model, suggest that a trade-off between the noise in the cell
geometry and the growth rate can lead to the self-regulation of the MTMT
timing. The MTMT cascades synchronous emergence of hydrodynamic fields,
actively enhancing the micro-environmental transport. Our results highlight how
interplay of phenotypic noise triggers emergent deterministic properties, and
reveal the role of multifield topology--of the colony structure and
hydrodynamics--to insulate confluent systems from the inherent noise associated
with natural cell-environment settings.


Abstract:
  Within the field of instance segmentation, most of the state-of-the-art deep
learning networks rely nowadays on cascade architectures, where multiple object
detectors are trained sequentially, re-sampling the ground truth at each step.
This offers a solution to the problem of exponentially vanishing positive
samples. However, it also translates into an increase in network complexity in
terms of the number of parameters. To address this issue, we propose
Recursively Refined R-CNN (R^3-CNN) which avoids duplicates by introducing a
loop mechanism instead. At the same time, it achieves a quality boost using a
recursive re-sampling technique, where a specific IoU quality is utilized in
each recursion to eventually equally cover the positive spectrum. Our
experiments highlight the specific encoding of the loop mechanism in the
weights, requiring its usage at inference time. The R^3-CNN architecture is
able to surpass the recently proposed HTC model, while reducing the number of
parameters significantly. Experiments on COCO minival 2017 dataset show
performance boost independently from the utilized baseline model. The code is
available online at https://github.com/IMPLabUniPr/mmdetection/tree/r3_cnn.


Abstract:
  Using a phase-field model which incorporates enhanced diffusion at the
nanowire surfaces, we study the effect of different parameters on the stability
of intersecting nanowires. Our study shows that at the intersection of
nanowires, sintering (curvature driven material flow) leads to the formation of
junctions. These junctions act the initiators of nanowire break-up. The
subsequent break-ups take place due to Rayleigh instability at the arms away
from these junctions. Finally, at long time scales, the fragments coarsen due
to the differences in sizes. The radii of the nanowires that form the junction,
the difference in size of the intersecting nanowires and the angle of
intersection play a dominant role in determining the kinetics of break-up while
the density of intersections has little or no effect on the kinetics. We
rationalise our results using maps of (i) mean curvatures (and, hence, chemical
potentials), and, (ii) Interfacial Shape Distributions (ISDs) (which are based
on probability densities associated with different combinations of the two
principal curvatures). Finally, we use the moment of inertia tensor to
characterise the (non-spherical) shapes and morphologies of (central) nanowire
fragments at the junctions.


Abstract:
  Learning meaningful representations of data is an important aspect of machine
learning and has recently been successfully applied to many domains like
language understanding or computer vision. Instead of training a model for one
specific task, representation learning is about training a model to capture all
useful information in the underlying data and make it accessible for a
predictor. For predictive process analytics, it is essential to have all
explanatory characteristics of a process instance available when making
predictions about the future, as well as for clustering and anomaly detection.
Due to the large variety of perspectives and types within business process
data, generating a good representation is a challenging task. In this paper, we
propose a novel approach for representation learning of business process
instances which can process and combine most perspectives in an event log. In
conjunction with a self-supervised pre-training method, we show the
capabilities of the approach through a visualization of the representation
space and case retrieval. Furthermore, the pre-trained model is fine-tuned to
multiple process prediction tasks and demonstrates its effectiveness in
comparison with existing approaches.


Abstract:
  This is a tutorial and survey paper on kernels, kernel methods, and related
fields. We start with reviewing the history of kernels in functional analysis
and machine learning. Then, Mercer kernel, Hilbert and Banach spaces,
Reproducing Kernel Hilbert Space (RKHS), Mercer's theorem and its proof,
frequently used kernels, kernel construction from distance metric, important
classes of kernels (including bounded, integrally positive definite, universal,
stationary, and characteristic kernels), kernel centering and normalization,
and eigenfunctions are explained in detail. Then, we introduce types of use of
kernels in machine learning including kernel methods (such as kernel support
vector machines), kernel learning by semi-definite programming, Hilbert-Schmidt
independence criterion, maximum mean discrepancy, kernel mean embedding, and
kernel dimensionality reduction. We also cover rank and factorization of kernel
matrix as well as the approximation of eigenfunctions and kernels using the
Nystr{\"o}m method. This paper can be useful for various fields of science
including machine learning, dimensionality reduction, functional analysis in
mathematics, and mathematical physics in quantum mechanics.


Abstract:
  Smartphones have enabled effortless capturing and sharing of documents in
digital form. The documents, however, often undergo various types of
degradation due to aging, stains, or shortcoming of capturing environment such
as shadow, non-uniform lighting, etc., which reduces the comprehensibility of
the document images. In this work, we consider the problem of document image
cleanup on embedded applications such as smartphone apps, which usually have
memory, energy, and latency limitations due to the device and/or for best human
user experience. We propose a light-weight encoder decoder based convolutional
neural network architecture for removing the noisy elements from document
images. To compensate for generalization performance with a low network
capacity, we incorporate the perceptual loss for knowledge transfer from
pre-trained deep CNN network in our loss function. In terms of the number of
parameters and product-sum operations, our models are 65-1030 and 3-27 times,
respectively, smaller than existing state-of-the-art document enhancement
models. Overall, the proposed models offer a favorable resource versus accuracy
trade-off and we empirically illustrate the efficacy of our approach on several
real-world benchmark datasets.


Abstract:
  In grammar-based compression a string is represented by a context-free
grammar, also called a straight-line program (SLP), that generates only that
string. We refine a recent balancing result stating that one can transform an
SLP of size $g$ in linear time into an equivalent SLP of size $O(g)$ so that
the height of the unique derivation tree is $O(\log N)$ where $N$ is the length
of the represented string (FOCS 2019). We introduce a new class of balanced
SLPs, called contracting SLPs, where for every rule $A \to \beta_1 \dots
\beta_k$ the string length of every variable $\beta_i$ on the right-hand side
is smaller by a constant factor than the string length of $A$. In particular,
the derivation tree of a contracting SLP has the property that every subtree
has logarithmic height in its leaf size. We show that a given SLP of size $g$
can be transformed in linear time into an equivalent contracting SLP of size
$O(g)$ with rules of constant length.
  We present an application to the navigation problem in compressed unranked
trees, represented by forest straight-line programs (FSLPs). We extend a linear
space data structure by Reh and Sieber (2020) by the operation of moving to the
$i$-th child in time $O(\log d)$ where $d$ is the degree of the current node.
Contracting SLPs are also applied to the finger search problem over
SLP-compressed strings where one wants to access positions near to a
pre-specified finger position, ideally in $O(\log d)$ time where $d$ is the
distance between the accessed position and the finger. We give a linear space
solution where one can access symbols or move the finger in time $O(\log d +
\log^{(t)} N)$ for any constant $t$ where $\log^{(t)} N$ is the $t$-fold
logarithm of $N$. This improves a previous solution by Bille, Christiansen,
Cording, and G{\o}rtz (2018) with access/move time $O(\log d + \log \log N)$.


Abstract:
  For graphs $G$ and $H$, we say that $G$ is $H$-free if it does not contain
$H$ as an induced subgraph. Already in the early 1980s Alekseev observed that
if $H$ is connected, then the \textsc{Max Weight Independent Set} problem
(MWIS) remains \textsc{NP}-hard in $H$-free graphs, unless $H$ is a path or a
subdivided claw, i.e., a graph obtained from the three-leaf star by subdividing
each edge some number of times (possibly zero). Since then determining the
complexity of MWIS in these remaining cases is one of the most important
problems in algorithmic graph theory.
  A general belief is that the problem is polynomial-time solvable, which is
witnessed by algorithmic results for graphs excluding some small paths or
subdivided claws. A more conclusive evidence was given by the recent
breakthrough result by Gartland and Lokshtanov [FOCS 2020]: They proved that
MWIS can be solved in quasipolynomial time in $H$-free graphs, where $H$ is any
fixed path. If $H$ is an arbitrary subdivided claw, we know much less: The
problem admits a QPTAS and a subexponential-time algorithm [Chudnovsky et al.,
SODA 2019].
  In this paper we make an important step towards solving the problem by
showing that for any subdivided claw $H$, MWIS is polynomial-time solvable in
$H$-free graphs of bounded degree.


Abstract:
  Adapter-based tuning has recently arisen as an alternative to fine-tuning. It
works by adding light-weight adapter modules to a pretrained language model
(PrLM) and only updating the parameters of adapter modules when learning on a
downstream task. As such, it adds only a few trainable parameters per new task,
allowing a high degree of parameter sharing. Prior studies have shown that
adapter-based tuning often achieves comparable results to fine-tuning. However,
existing work only focuses on the parameter-efficient aspect of adapter-based
tuning while lacking further investigation on its effectiveness. In this paper,
we study the latter. We first show that adapter-based tuning better mitigates
forgetting issues than fine-tuning since it yields representations with less
deviation from those generated by the initial PrLM. We then empirically compare
the two tuning methods on several downstream NLP tasks and settings. We
demonstrate that 1) adapter-based tuning outperforms fine-tuning on
low-resource and cross-lingual tasks; 2) it is more robust to overfitting and
less sensitive to changes in learning rates.


Abstract:
  Functional autonomous systems often realize complex tasks by utilizing state
machines comprised of discrete primitive behaviors and transitions between
these behaviors. This architecture has been widely studied in the context of
quasi-static and dynamics-independent systems. However, applications of this
concept to dynamical systems are relatively sparse, despite extensive research
on individual dynamic primitive behaviors, which we refer to as "motion
primitives." This paper formalizes a process to determine dynamic-state aware
conditions for transitions between motion primitives in the context of safety.
The result is framed as a "motion primitive graph" that can be traversed by
standard graph search and planning algorithms to realize functional autonomy.
To demonstrate this framework, dynamic motion primitives -- including standing
up, walking, and jumping -- and the transitions between these behaviors are
experimentally realized on a quadrupedal robot.


Abstract:
  We consider the problem of explainable $k$-medians and $k$-means introduced
by Dasgupta, Frost, Moshkovitz, and Rashtchian~(ICML 2020). In this problem,
our goal is to find a threshold decision tree that partitions data into $k$
clusters and minimizes the $k$-medians or $k$-means objective. The obtained
clustering is easy to interpret because every decision node of a threshold tree
splits data based on a single feature into two groups. We propose a new
algorithm for this problem which is $\tilde O(\log k)$ competitive with
$k$-medians with $\ell_1$ norm and $\tilde O(k)$ competitive with $k$-means.
This is an improvement over the previous guarantees of $O(k)$ and $O(k^2)$ by
Dasgupta et al (2020). We also provide a new algorithm which is $O(\log^{3/2}
k)$ competitive for $k$-medians with $\ell_2$ norm. Our first algorithm is
near-optimal: Dasgupta et al (2020) showed a lower bound of $\Omega(\log k)$
for $k$-medians; in this work, we prove a lower bound of $\tilde\Omega(k)$ for
$k$-means. We also provide a lower bound of $\Omega(\log k)$ for $k$-medians
with $\ell_2$ norm.


Abstract:
  We study the problem of building entity tagging systems by using a few rules
as weak supervision. Previous methods mostly focus on disambiguation entity
types based on contexts and expert-provided rules, while assuming entity spans
are given. In this work, we propose a novel method TALLOR that bootstraps
high-quality logical rules to train a neural tagger in a fully automated
manner. Specifically, we introduce compound rules that are composed from simple
rules to increase the precision of boundary detection and generate more diverse
pseudo labels. We further design a dynamic label selection strategy to ensure
pseudo label quality and therefore avoid overfitting the neural tagger.
Experiments on three datasets demonstrate that our method outperforms other
weakly supervised methods and even rivals a state-of-the-art distantly
supervised tagger with a lexicon of over 2,000 terms when starting from only 20
simple rules. Our method can serve as a tool for rapidly building taggers in
emerging domains and tasks. Case studies show that learned rules can
potentially explain the predicted entities.


Abstract:
  User authentication through gait analysis is a promising application of
discriminative neural networks -- particularly due to the ubiquity of the
primary sources of gait accelerometry, in-pocket cellphones. However,
conventional machine learning models are often too large and computationally
expensive to enable inference on low-resource mobile devices. We propose that
binarized neural networks can act as robust discriminators, maintaining both an
acceptable level of accuracy while also dramatically decreasing memory
requirements, thereby enabling on-device inference. To this end, we propose
BiPedalNet, a compact CNN that nearly matches the state-of-the-art on the
Padova gait dataset, with only 1/32 of the memory overhead.


Abstract:
  We study nodal sets of Steklov eigenfunctions in a bounded domain with
$\mathcal{C}^2$ boundary. Our first result is a lower bound for the Hausdorff
measure of the nodal set: we show that for $u_{\lambda}$ a Steklov
eigenfunction, with eigenvalue $\lambda\neq 0$,
$\mathcal{H}^{d-1}(\{u_{\lambda}=0\})\geq c_{\Omega}$, where $c_{\Omega}$ is
independent of $\lambda$. We also prove an almost sharp upper bound, namely
$\mathcal{H}^{d-1}(\{u_{\lambda}=0\})\leq C_{\Omega}\lambda\log(\lambda+e)$.


Abstract:
  Document-level relation extraction aims to extract relations among multiple
entity pairs from a document. Previously proposed graph-based or
transformer-based models utilize the entities independently, regardless of
global information among relational triples. This paper approaches the problem
by predicting an entity-level relation matrix to capture local and global
information, parallel to the semantic segmentation task in computer vision.
Herein, we propose a Document U-shaped Network for document-level relation
extraction. Specifically, we leverage an encoder module to capture the context
information of entities and a U-shaped segmentation module over the image-style
feature map to capture global interdependency among triples. Experimental
results show that our approach can obtain state-of-the-art performance on three
benchmark datasets DocRED, CDR, and GDA.


Abstract:
  We show that the threshold condition for two pump photons to convert into a
pair of the sideband ones in Kerr microresonators with high-quality factors
breaks the pump laser parameter space into a sequence of narrow in frequency
and broad in power Arnold tongues. Instability tongues become a dominant
feature in resonators with the finesse dispersion parameter close to and above
one. As pump power is increased, the tongues expand and cross by forming a line
of cusps, i.e., the threshold of complexity, where more sideband pairs become
unstable. We elaborate theory for the tongues and threshold of complexity, and
report the synchronisation and frequency-domain symmetry breaking effects
inside the tongues.


Abstract:
  Many existing mortality models follow the framework of classical factor
models, such as the Lee-Carter model and its variants. Latent common factors in
factor models are defined as time-related mortality indices (such as $\kappa_t$
in the Lee-Carter model). Factor loadings, which capture the linear
relationship between age variables and latent common factors (such as $\beta_x$
in the Lee-Carter model), are assumed to be time-invariant in the classical
framework. This assumption is usually too restrictive in reality as mortality
datasets typically span a long period of time. Driving forces such as medical
improvement of certain diseases, environmental changes and technological
progress may significantly influence the relationship of different variables.
In this paper, we first develop a factor model with time-varying factor
loadings (time-varying factor model) as an extension of the classical factor
model for mortality modelling. Two forecasting methods to extrapolate the
factor loadings, the local regression method and the naive method, are proposed
for the time-varying factor model. From the empirical data analysis, we find
that the new model can capture the empirical feature of time-varying factor
loadings and improve mortality forecasting over different horizons and
countries. Further, we propose a novel approach based on change point analysis
to estimate the optimal `boundary' between short-term and long-term
forecasting, which is favoured by the local linear regression and naive method,
respectively. Additionally, simulation studies are provided to show the
performance of the time-varying factor model under various scenarios.


Abstract:
  As the roadside perception plays an increasingly significant role in the
Connected Automated Vehicle Highway(CAVH) technologies, there are immediate
needs of challenging real-world roadside datasets for bench marking and
training various computer vision tasks such as 2D/3D object detection and
multi-sensor fusion. In this paper, we firstly introduce a challenging
BAAI-VANJEE roadside dataset which consist of LiDAR data and RGB images
collected by VANJEE smart base station placed on the roadside about 4.5m high.
This dataset contains 2500 frames of LiDAR data, 5000 frames of RGB images,
including 20% collected at the same time. It also contains 12 classes of
objects, 74K 3D object annotations and 105K 2D object annotations. By providing
a real complex urban intersections and highway scenes, we expect the
BAAI-VANJEE roadside dataset will actively assist the academic and industrial
circles to accelerate the innovation research and achievement transformation in
the field of intelligent transportation in big data era.


Abstract:
  Recent works on voice conversion (VC) focus on preserving the rhythm and the
intonation as well as the linguistic content. To preserve these features from
the source, we decompose current non-parallel VC systems into two encoders and
one decoder. We analyze each module with several experiments and reassemble the
best components to propose Assem-VC, a new state-of-the-art any-to-many
non-parallel VC system. We also examine that PPG and Cotatron features are
speaker-dependent, and attempt to remove speaker identity with adversarial
training. Code and audio samples are available at
https://github.com/mindslab-ai/assem-vc.


Abstract:
  We calculate the resummed perturbative free energy of ${\cal N}=4$
supersymmetric Yang-Mills in four spacetime dimensions ($\text{SYM}_{4,4}$)
through second order in the 't Hooft coupling $\lambda$ at finite temperature
and zero chemical potential. Our final result is ultraviolet finite and all
infrared divergences generated at three-loop level are canceled by summing over
$\text{SYM}_{4,4}$ ring diagrams. Non-analytic terms at ${\cal
O}({\lambda}^{3/2}) $ and $ {\cal O}({\lambda}^2 \log\lambda )$ are generated
by dressing the $A_0$ and scalar propagators. The gauge-field Debye mass $m_D$
and the scalar thermal mass $M$ are determined from their corresponding
finite-temperature self-energies. Based on this, we obtain the three-loop
thermodynamic functions of $\text{SYM}_{4,4}$ to ${\cal O}(\lambda^2)$. We
compare our final result with prior results obtained in the weak- and
strong-coupling limits and construct a generalized Pad\'{e} approximant that
interpolates between the weak-coupling result and the large-$N_c$
strong-coupling result. Our results suggest that the ${\cal O}(\lambda^2)$
weak-coupling result for the scaled entropy density is a quantitatively
reliable approximation to the scaled entropy density for $0 \leq \lambda
\lesssim 2$.


Abstract:
  In this paper, we study the shifting zeros with its heights and an analogue
to difference radical. We focus on the Stothers-Mason theorem by using falling
factorials. As applications, we discuss the difference version of the Fermat
type functional equations. Some examples are given.


Abstract:
  We study a system of Skorokhod stochastic differential equations (SDEs)
modeling the pairwise dispersion (in spatial dimension $d=2$) of heavy
particles transported by a rough self-similar, turbulent flow with H\"{o}lder
exponent $h\in (0,1)$. Under the assumption that $h>0$ is sufficiently small,
we use Lyapunov methods and control theory to show that the Markovian system is
nonexplosive and has a unique, exponentially attractive invariant probability
measure. Furthermore, our Lyapunov construction is radially sharp and gives
partial confirmation on a predicted asymptotic behavior with respect to the
H\"{o}lder exponent $h$ of the invariant probability measure. A physical
interpretation of the asymptotics is that intermittent clustering is weakened
when the carrier flow is sufficiently rough.


Abstract:
  We study the entanglement production for Dirac and Klein-Gordon fields in an
expanding spacetime characterized by the presence of torsion. Torsion is here
considered according to the Einstein-Cartan theory with a conformally flat
Friedmann-Robertson-Walker spacetime. In this framework, torsion is seen as an
external field, fulfilling precise constraints directly got from the
cosmological constant principle. For Dirac field, we find that torsion
increases the amount of entanglement. This turns out to be particularly evident
for small values of particle momentum. We discuss the roles of Pauli exclusion
principle in view of our results, and, in particular, we propose an
interpretation of the two maxima that occur for the entanglement entropy in
presence of torsion. For Klein-Gordon field, and differently from the Dirac
case, the model can be exactly solved by adopting the same scale factor as in
the Dirac case. Again, we show how torsion affects the amount of entanglement,
providing a robust physical motivation behind the increase or decrease of
entanglement entropy. A direct comparison of our findings is also discussed in
view of previous results derived in absence of torsion. To this end, we give
prominence on how our expectations would change in terms of the coupling
between torsion and the scale factor for both Dirac and Klein-Gordon fields.


Abstract:
  Recent development of Deep Reinforcement Learning (DRL) has demonstrated
superior performance of neural networks in solving challenging problems with
large or even continuous state spaces. One specific approach is to deploy
neural networks to approximate value functions by minimising the Mean Squared
Bellman Error (MSBE) function. Despite great successes of DRL, development of
reliable and efficient numerical algorithms to minimise the MSBE is still of
great scientific interest and practical demand. Such a challenge is partially
due to the underlying optimisation problem being highly non-convex or using
incomplete gradient information as done in Semi-Gradient algorithms. In this
work, we analyse the MSBE from a smooth optimisation perspective and develop an
efficient Approximate Newton's algorithm.
  First, we conduct a critical point analysis of the error function and provide
technical insights on optimisation and design choices for neural networks. When
the existence of global minima is assumed and the objective fulfils certain
conditions, suboptimal local minima can be avoided when using over-parametrised
neural networks. We construct a Gauss Newton Residual Gradient algorithm based
on the analysis in two variations. The first variation applies to discrete
state spaces and exact learning. We confirm theoretical properties of this
algorithm such as being locally quadratically convergent to a global minimum
numerically. The second employs sampling and can be used in the continuous
setting. We demonstrate feasibility and generalisation capabilities of the
proposed algorithm empirically using continuous control problems and provide a
numerical verification of our critical point analysis. We outline the
difficulties of combining Semi-Gradient approaches with Hessian information. To
benefit from second-order information complete derivatives of the MSBE must be
considered during training.


Abstract:
  The influence of zinc ions on the thermochromic properties of polycrystalline
Cu$_{1-x}$Zn$_x$MoO$_4$ ($x$=0.10, 0.50, 0.90) solid solutions was studied by
X-ray absorption spectroscopy at the Cu, Zn and Mo K-edges. Detailed structural
information on the local environment of metal ions was obtained from the
simultaneous analysis of EXAFS spectra measured at three metal absorption edges
using the reverse Monte Carlo method. Thermochromic phase transition with the
hysteretic behaviour between $\alpha$ and $\gamma$ phases was observed in
Cu$_{0.90}$Zn$_{0.10}$MoO$_4$ solid solution. It was found that the local
environment of molybdenum ions is most susceptible to the substitution of
copper for zinc and, upon cooling, transforms from tetrahedral MoO$_4$ to
distorted octahedral MoO$_6$.


Abstract:
  Data movement between the CPU and main memory is a first-order obstacle
against improving performance, scalability, and energy efficiency in modern
systems. Computer systems employ a range of techniques to reduce overheads tied
to data movement, spanning from traditional mechanisms (e.g., deep multi-level
cache hierarchies, aggressive hardware prefetchers) to emerging techniques such
as Near-Data Processing (NDP), where some computation is moved close to memory.
Our goal is to methodically identify potential sources of data movement over a
broad set of applications and to comprehensively compare traditional
compute-centric data movement mitigation techniques to more memory-centric
techniques, thereby developing a rigorous understanding of the best techniques
to mitigate each source of data movement.
  With this goal in mind, we perform the first large-scale characterization of
a wide variety of applications, across a wide range of application domains, to
identify fundamental program properties that lead to data movement to/from main
memory. We develop the first systematic methodology to classify applications
based on the sources contributing to data movement bottlenecks. From our
large-scale characterization of 77K functions across 345 applications, we
select 144 functions to form the first open-source benchmark suite (DAMOV) for
main memory data movement studies. We select a diverse range of functions that
(1) represent different types of data movement bottlenecks, and (2) come from a
wide range of application domains. Using NDP as a case study, we identify new
insights about the different data movement bottlenecks and use these insights
to determine the most suitable data movement mitigation mechanism for a
particular application. We open-source DAMOV and the complete source code for
our new characterization methodology at https://github.com/CMU-SAFARI/DAMOV.


Abstract:
  Mobile health (mHealth) apps such as menstrual trackers provide a rich source
of self-tracked health observations that can be leveraged for health-relevant
research. However, such data streams have questionable reliability since they
hinge on user adherence to the app. Therefore, it is crucial for researchers to
separate true behavior from self-tracking artifacts. By taking a machine
learning approach to modeling self-tracked cycle lengths, we can both make more
informed predictions and learn the underlying structure of the observed data.
In this work, we propose and evaluate a hierarchical, generative model for
predicting next cycle length based on previously-tracked cycle lengths that
accounts explicitly for the possibility of users skipping tracking their
period. Our model offers several advantages: 1) accounting explicitly for
self-tracking artifacts yields better prediction accuracy as likelihood of
skipping increases; 2) because it is a generative model, predictions can be
updated online as a given cycle evolves, and we can gain interpretable insight
into how these predictions change over time; and 3) its hierarchical nature
enables modeling of an individual's cycle length history while incorporating
population-level information. Our experiments using mHealth cycle length data
encompassing over 186,000 menstruators with over 2 million natural menstrual
cycles show that our method yields state-of-the-art performance against neural
network-based and summary statistic-based baselines, while providing insights
on disentangling menstrual patterns from self-tracking artifacts. This work can
benefit users, mHealth app developers, and researchers in better understanding
cycle patterns and user adherence.


Abstract:
  Bellwether effect refers to the existence of exemplary projects (called the
Bellwether) within a historical dataset to be used for improved prediction
performance. Recent studies have shown an implicit assumption of using recently
completed projects (referred to as moving window) for improved prediction
accuracy. In this paper, we investigate the Bellwether effect on software
effort estimation accuracy using moving windows. The existence of the
Bellwether was empirically proven based on six postulations. We apply
statistical stratification and Markov chain methodology to select the
Bellwether moving window. The resulting Bellwether moving window is used to
predict the software effort of a new project. Empirical results show that
Bellwether effect exist in chronological datasets with a set of exemplary and
recently completed projects representing the Bellwether moving window. Result
from this study has shown that the use of Bellwether moving window with the
Gaussian weighting function significantly improve the prediction accuracy.


Abstract:
  Pre-trained Transformer language models (LM) have become go-to text
representation encoders. Prior research fine-tunes deep LMs to encode text
sequences such as sentences and passages into single dense vector
representations for efficient text comparison and retrieval. However, dense
encoders require a lot of data and sophisticated techniques to effectively
train and suffer in low data situations. This paper finds a key reason is that
standard LMs' internal attention structure is not ready-to-use for dense
encoders, which needs to aggregate text information into the dense
representation. We propose to pre-train towards dense encoder with a novel
Transformer architecture, Condenser, where LM prediction CONditions on DENSE
Representation. Our experiments show Condenser improves over standard LM by
large margins on various text retrieval and similarity tasks.


Abstract:
  We develop a quenched thermodynamic formalism for open random dynamical
systems generated by finitely branched, piecewise-monotone mappings of the
interval. The openness refers to the presence of holes in the interval, which
terminate trajectories once they enter; the holes may also be random. Our
random driving is generated by an invertible, ergodic, measure-preserving
transformation $\sigma$ on a probability space $(\Omega,\mathscr{F},m)$. For
each $\omega\in\Omega$ we associate a piecewise-monotone, surjective map
$T_\omega:I\to I$, and a hole $H_\omega\subset I$; the map $T_\omega$, the
random potential $\varphi_\omega$, and the hole $H_\omega$ generate the
corresponding open transfer operator $\mathcal{L}_\omega$. For a contracting
potential, under a condition on the open random dynamics in the spirit of
Liverani--Maume-Deschamps, we prove there exists a unique random probability
measure $\nu_\omega$ supported on the survivor set ${X}_{\omega,\infty}$
satisfying $\nu_{\sigma(\omega)}(\mathcal{L}_\omega
f)=\lambda_\omega\nu_\omega(f)$. We also prove the existence of a unique random
family of functions $q_\omega$ that satisfy $\mathcal{L}_\omega
q_\omega=\lambda_\omega q_{\sigma(\omega)}$. These yield an ergodic random
invariant measure $\mu=\nu q$ supported on the global survivor set, while $q$
combined with the random closed conformal measure yields a unique random
absolutely continuous conditional invariant measure (RACCIM) $\eta$ supported
on $I$. We prove quasi-compactness of the transfer operator cocycle and
exponential decay of correlations for $\mu$. Finally, the escape rates of the
random closed conformal measure and the RACCIM $\eta$ coincide, and are given
in terms of the expected pressure, as is the Hausdorff dimension of the
surviving set $X_{\omega,\infty}$. We provide examples of our general theory,
including random $\beta$-transformations and random Lasota-Yorke maps.


Abstract:
  We present here a version of the Artin-Schreier polynomial that works in any
characteristic. Let $C_p$ be the cyclic group of prime order $p$. Equivalently,
we prove one can lift degree $p$ cyclic $C_p$ extensions over local rings $R,M$
where $R/M$ has characteristic $p$ and $R$ has arbitrary characterstic.
  Let $\rho \in \Bbb C$ be a primitive $p$ root of one. We first consider the
case $R$ has a primitive $p$ root of one, by which we mean that there is a
given homomorphism $f: \Bbb Z[\rho] \to R$. In this context we can write a
specific polynomial which generalizes the Artin-Schreier polynomial. We next
consider arbtitrary $R$ and construct a mixed characteristic "generic" Galois
extension that proves the lifting result, but here we do not supply a
polynomial.
  It is useful to view these results in terms of Galois actions. If $C_p$ is
generated by $\sigma$, then Artin-Schreier polynomials describe $C_p$ Galois
extensions generated by elements $\theta$ where $\sigma(\theta) = \theta + 1$.
If $\rho' = f(\rho)$, then our lifted Galois action is $\sigma(\theta) =
\rho'\theta + 1$. In section two we descend this extension to rings without
root of one. In section three we use this new description of cyclic extensions,
give the obvious description of the corresponding cyclic algebras, and then
define a new algebra which specializes to general differential crossed products
in characteristic $p$ but which are cyclic in characteristic not $p$. The
algebra in question is generated by $x,y$ subject to the relation $xy -
\rho{yx} = 1$.


Abstract:
  Although the adoption rate of deep neural networks (DNNs) has tremendously
increased in recent years, a solution for their vulnerability against
adversarial examples has not yet been found. As a result, substantial research
efforts are dedicated to fix this weakness, with many studies typically using a
subset of source images to generate adversarial examples, treating every image
in this subset as equal. We demonstrate that, in fact, not every source image
is equally suited for this kind of assessment. To do so, we devise a
large-scale model-to-model transferability scenario for which we meticulously
analyze the properties of adversarial examples, generated from every suitable
source image in ImageNet by making use of three of the most frequently deployed
attacks. In this transferability scenario, which involves seven distinct DNN
models, including the recently proposed vision transformers, we reveal that it
is possible to have a difference of up to $12.5\%$ in model-to-model
transferability success, $1.01$ in average $L_2$ perturbation, and $0.03$
($8/225$) in average $L_{\infty}$ perturbation when $1,000$ source images are
sampled randomly among all suitable candidates. We then take one of the first
steps in evaluating the robustness of images used to create adversarial
examples, proposing a number of simple but effective methods to identify
unsuitable source images, thus making it possible to mitigate extreme cases in
experimentation and support high-quality benchmarking.


Abstract:
  The event sequence of many diverse systems is represented as a sequence of
discrete events in a continuous space. Examples of such an event sequence are
earthquake aftershock events, financial transactions, e-commerce transactions,
social network activity of a user, and the user's web search pattern. Finding
such an intricate pattern helps discover which event will occur in the future
and when it will occur. A Hawkes process is a mathematical tool used for
modeling such time series discrete events. Traditionally, the Hawkes process
uses a critical component for modeling data as an intensity function with a
parameterized kernel function. The Hawkes process's intensity function involves
two components: the background intensity and the effect of events' history.
However, such parameterized assumption can not capture future event
characteristics using past events data precisely due to bias in modeling kernel
function. This paper explores the recent advancement using novel deep
learning-based methods to model kernel function to remove such parametrized
kernel function. In the end, we will give potential future research directions
to improve modeling using the Hawkes process.


Abstract:
  In this work, we build upon the Active Inference (AIF) and Predictive Coding
(PC) frameworks to propose a neural architecture comprising a generative model
for sensory prediction, and a distinct generative model for motor trajectories.
We highlight how sequences of sensory predictions can act as rails guiding
learning, control and online adaptation of motor trajectories. We furthermore
inquire the effects of bidirectional interactions between the motor and the
visual modules. The architecture is tested on the control of a simulated
robotic arm learning to reproduce handwritten letters.


Abstract:
  The capacity of line networks with buffer size constraints is an open, but
practically important problem. In this paper, we consider line networks formed
by discrete memoryless channels, and study the achievable rate of batched
codes, which include several existing coding schemes studied in literature for
line networks as special cases. A batched code has an outer code and an inner
code. The outer code encodes the information messages into batches, each of
which is a group of coded symbols, while the inner code performs recoding on
the symbols belonging to the same batch at all nodes that forwarding a batch.
Batched codes enable us to impose various block-length and buffer size
constraints by tuning coding parameters. Using a technique that captures the
communication bottleneck of line networks, we derive upper bounds on the
achievable rates of batched codes as functions of line network length for
several coding parameter sets. We also discuss specific recoding schemes that
can achieve rates of the same order of the line network length for these coding
parameter sets. Our results shed light on how to design large multi-hop network
communications in practical scenarios using a unified coding framework instead
of the traditional layered approach.


Abstract:
  Isospin-1/2 $D\pi$ scattering amplitudes are computed using lattice QCD,
working in a single volume of approximately $(3.6\; \mathrm{fm})^3$ and with a
light quark mass corresponding to $m_\pi\approx239$ MeV. The spectrum of the
elastic $D\pi$ energy region is computed yielding 20 energy levels. Using the
L\"uscher finite-volume quantisation condition, these energies are translated
into constraints on the infinite-volume scattering amplitudes and hence enable
us to map out the energy dependence of elastic $D\pi$ scattering. By
analytically continuing a range of scattering amplitudes, a $D_0^\ast$
resonance pole is consistently found strongly coupled to the $S$-wave $D\pi$
channel, with a mass $m\approx 2200$ MeV and a width $\Gamma\approx400$ MeV.
Combined with earlier work investigating the $D_{s0}^\ast$, and $D_0^\ast$ with
heavier light quarks, similar couplings between each of these scalar states and
their relevant meson-meson scattering channels are determined. The mass of the
$D_0^\ast$ is consistently found well below that of the $D_{s0}^\ast$, in
contrast to the currently reported experimental result.


Abstract:
  We prove that a quasi-finite endomorphism of an algebraic variety over an
algebraically closed field of characteristic zero, that is injective on the
complement of a closed subvariety, is an automorphism. We also prove that an
endomorphism of complex algebraic variety that is injective on the complement
of a closed subvariety of codimension at least $2$, is an automorphism.


Abstract:
  Despite their simple formulation, short range classical antiferromagnetic
Ising models on frustrated lattices give rise to exotic phases of matter, in
particular due to their macroscopic ground state degeneracy. Recent experiments
on artificial spin systems comprising arrays of chirally coupled nanomagnets
provide a significant strengthening of the nearest neighbour couplings compared
to systems with dipolar-coupled nanomagnets. This opens the way to design
artificial spin systems emulating Ising models with nearest neighbour
couplings. In this paper, we compare the results of an extensive investigation
with tensor network and Monte Carlo simulations of the nearest- and
further-neighbour ($J_1-J_2-J_{3||}$) kagome Ising antiferromagnet with the
experimental spin-spin correlations of a kagome lattice of chirally coupled
nanomagnets. Even though the ratios between the further neighbour couplings and
the nearest neighbour coupling estimated from micromagnetic simulations are
much smaller than for dipolar-coupled nanomagnets, we show that they still play
an essential role in the selection of the correlations.


Abstract:
  The Internet of Things (IoT) is affecting national innovation ecosystems and
the approach of organizations to innovation and how they create and capture
value in everyday business activities. The Internet of Things (IoT), is
disruptive, and it will change the manner in which human resources are
developed and managed, calling for a new and adaptive human resource
development approach. The Classical Internet communication form is human-human.
The prospect of IoT is that every object will have a unique way of
identification and can be addressed so that every object can be connected. The
communication forms will expand from human-human to human-human, human-thing,
and thing-thing. This will bring a new challenge to how Human Resource
Development (HRD) is practiced. This paper provides an overview of the Internet
of Things and conceptualizes the role of HRD in the age of the Internet of
Things. Keywords:


Abstract:
  It has been demonstrated many times that the behavior of the human visual
system is connected to the statistics of natural images. Since machine learning
relies on the statistics of training data as well, the above connection has
interesting implications when using perceptual distances (which mimic the
behavior of the human visual system) as a loss function. In this paper, we aim
to unravel the non-trivial relationships between the probability distribution
of the data, perceptual distances, and unsupervised machine learning. To this
end, we show that perceptual sensitivity is correlated with the probability of
an image in its close neighborhood. We also explore the relation between
distances induced by autoencoders and the probability distribution of the
training data, as well as how these induced distances are correlated with human
perception. Finally, we find perceptual distances do not always lead to
noticeable gains in performance over Euclidean distance in common image
processing tasks, except when data is scarce and the perceptual distance
provides regularization. We propose this may be due to a \emph{double-counting}
effect of the image statistics, once in the perceptual distance and once in the
training procedure.


Abstract:
  The nodal line semimetals have attracted much attention due to their unique
topological electronic structure and exotic physical properties. A genuine
nodal line semimetal is qualified by the presence of Dirac nodes along a line
in the momentum space that are protected against the spin-orbit coupling. In
addition, it requires that the Dirac points lie close to the Fermi level
allowing to dictate the macroscopic physical properties. Although the material
realization of nodal line semimetals have been theoretically predicted in
numerous compounds, only a few of them have been experimentally verified and
the realization of a genuine nodal line semimetal is particularly rare. Here we
report the realization of a genuine nodal line semimetal in LaSbTe. We
investigated the electronic structure of LaSbTe by band structure calculations
and angle-resolved photoemission (ARPES) measurements. Taking spin-orbit
coupling into account, our band structure calculations predict that a nodal
line is formed in the boundary surface of the Brillouin zone which is robust
and lies close to the Fermi level. The Dirac nodes along the X-R line in
momentum space are directly observed in our ARPES measurements and the energies
of these Dirac nodes are all close to the Fermi level. These results constitute
clear evidence that LaSbTe is a genuine nodal line semimetal,providing a new
platform to explore for novel phenomena and possible applications associated
with the nodal line semimetals.


Abstract:
  When we experience an event, it feels like our previous experiences, our
interpretations of that event (e.g., aesthetics, emotions), and our current
state will determine how we will remember it. However, recent work has revealed
a strong sway of the visual world itself in influencing what we remember and
forget. Certain items -- including certain faces, words, images, and movements
-- are intrinsically memorable or forgettable across observers, regardless of
individual differences. Further, neuroimaging research has revealed that the
brain is sensitive to memorability both rapidly and automatically during late
perception. These strong consistencies in memory across people may reflect the
broad organizational principles of our sensory environment, and may reveal how
the brain prioritizes information before encoding items into memory. In this
chapter, I will discuss our current state-of-the-art understanding of
memorability for visual information, and what these findings imply about how we
perceive and remember visual events.


Abstract:
  In this work, we perform a systematical investigation about the possible
hidden and doubly heavy molecular states with open and hidden strangeness from
interactions of $D^{(*)}{\bar{D}}^{(*)}_{s}$/$B^{(*)}{\bar{B}}^{(*)}_{s}$,
${D}^{(*)}_{s}{\bar{D}}^{(*)}_{s}$/${{B}}^{(*)}_{s}{\bar{B}}^{(*)}_{s}$,
${D}^{(*)}D_{s}^{(*)}$/${B}^{(*)}B_{s}^{(*)}$, and
$D_{s}^{(*)}D_{s}^{(*)}$/$B_{s}^{(*)}B_{s}^{(*)}$ in a quasipotential
Bethe-Salpeter equation approach. The interactions of the systems considered
are described within the one-boson-exchange model, which includes exchanges of
light mesons and $J/\psi/\Upsilon$ meson. Possible molecular states are
searched for as poles of scattering amplitudes of the interactions considered.
The results suggest that recently observed $Z_{cs}(3985)$ can be assigned as a
molecular state of $D^*\bar{D}_s+D\bar{D}^*_s$, which is a partner of
$Z_c(3900)$ state as a $D\bar{D}^*$ molecular state. The calculation also
favors the existence of hidden heavy states $D_s\bar{D}_s/B_s\bar{B}_s$ with
spin parity $J^P=0^+$, $D_s\bar{D}^*_s/B_s\bar{B}^*_s$ with $1^{+}$, and
$D^*_s\bar{D}^*_s/B^*_s\bar{B}^*_s$ with $0^+$, $1^+$, and $2^+$. In the doubly
heavy sector, the bound states can be found from the interactions
$(D^*D_s+DD^*_s)/(B^*B_s+BB^*_s)$ with $1^+$, $D_s\bar{D}_s^*/B_s\bar{B}_s^*$
with $1^+$, $D^*D^*_s/B^*B^*_s$ with $1^+$ and $2^+$, and
$D^*_sD^*_s/B^*_sB^*_s$ with $1^+$ and $2^+$. Some other interactions are also
found attractive, but may be not strong enough to produce a bound state. The
results in this work are helpful for understanding the $Z_{cs}(3985)$, and
future experimental search for the new molecular states.


Abstract:
  We evaluate the masses and decay constants of the $0^+$ and $1^-$ open-charm
$(\bar{c}\bar{d})(us)$ tetraquarks and molecular states from QCD spectral sum
rules (QSSR) by using QCD Laplace sum rule (LSR). This method takes into
account the stability criteria where the factorized perturbative NLO
corrections and the contributions of quark and gluon condensates up to
dimension-6 in the OPE are included. We confront our results with the $D^- K^+$
invariant mass recently reported by LHCb from $B^+ \to D^+(D^- K^+)$ decays. We
expect that the resonance near the $D^- K^+$ threshold can be originated from
the $0^{+}(D^-K^+)$ molecule and/or $D^- K^+$ scattering. The $X_0(2900)$
scalar state and the resonance $X_J(3150)$ (if $J = 0$) can emerge from a
minimal mixing model, with a tiny mixing angle $\theta_0 \simeq (5.2 \pm
1.9)^0$, between a scalar Tetramole $({\cal T}_{\!\!{\cal M}0})$ (superposition
of nearly degenerated hypothetical molecules and compact tetraquarks states
with the same quantum numbers), having a mass $M_{{\cal T}_{\!\!{\cal M}0}} =
2743(18)$ MeV, and the first radial excitation of the $D^- K^+$ molecule with
mass $M_{(DK)_1} = 3678(310)$ MeV. In an analogous way, the $X_1(2900)$ and the
$X_J(3350)$ (if $J = 1$) could be a mixture between the vector Tetramole
$({\cal T}_{\!\!{\cal M}1})$, with a mass $M_{{\cal T}_{\!\!{\cal M}1}} =
2656(20)$ MeV, and its first radial excitation having a mass $M_{{\cal
T}_{\!\!{\cal M}1}} = 4592(141)$ MeV with an angle $\theta_0 \simeq (9.1 \pm
0.6)^0$. A (non)-confirmation of these statements requires experimental
findings of the quantum numbers of the resonances at $3150$ and $3350$ MeV.


Abstract:
  From new detailed experimental data, we found that the Radial Distribution
Function (RDF) of inertial particles in turbulence grows explosively with
$r^{-6}$ scaling as the collision radius is approached. We corrected a theory
by Yavuz et al. (Phys. Rev. Lett. 120, 244504 (2018)) based on hydrodynamic
interactions between pairs of weakly inertial particles, and demonstrate that
even this corrected theory cannot explain the observed RDF behavior. We explore
several alternative mechanisms for the discrepancy that were not included in
the theory and show that none of them are likely the explanation, suggesting
new, yet to be identified physical mechanisms are at play.


Abstract:
  Reflection is an often addressed design goal in Human-Computer Interaction
(HCI) research. An increasing number of artefacts for reflection have been
developed in recent years. However, evaluating if and how an interactive
technology helps a user reflect is still complex. This makes it difficult to
compare artefacts (or prototypes) for reflection, impeding future design
efforts. To address this issue, we developed the \emph{Technology-Supported
Reflection Inventory} (TSRI), which is a scale that evaluates how effectively a
system supports reflection. We first created a list of possible scale items
based on past work in defining reflection. The items were then reviewed by
experts. Next, we performed exploratory factor analysis to reduce the scale to
its final length of nine items. Subsequently, we confirmed test-retest validity
of our instrument, as well as its construct validity. The TSRI enables
researchers and practitioners to compare prototypes designed to support
reflection.


Abstract:
  We demonstrate a 13.3 MHz repetition rate, broadband THz source with
milliwatt-average power, obtained by collinear optical rectification of a
high-power Yb-doped thin-disk laser in the organic crystal BNA
(N-benzyl-2-methyl-4-nitroaniline). Our source reaches a maximum THz average
power of 0.95 mW with an optical-to-THz efficiency of 4e-4 and a spectral
bandwidth spanning up to 6 THz at -50 dB, driven by 2.4 W average power (after
an optical chopper with duty cycle of 10%), 85 fs-pulses. This high average
power excitation was possible without damaging the crystal by using a
diamond-heatsinked crystal with significantly improved thermal properties. To
the best of our knowledge, this result represents the highest THz average power
reported so far using the commercially available organic crystal BNA, showing
the potential of these crystals for high average power, high repetition rate
femtosecond excitation. The combination of high power, high dynamic range, high
repetition rate and broadband spectrum makes the demonstrated THz source highly
attractive to improve various time-domain spectroscopy applications.
Furthermore, we present a first exploration of the thermal behavior of BNA in
this excitation regime, showing that thermal effects are the main limitation in
average power scaling in these crystals.


Abstract:
  The problem of beam alignment (BA) in a cell-free massive multiple-input
multiple-output (CF-mMIMO) system operating at millimeter wave (mmWaves)
carrier frequencies is considered in this paper. Two estimation algorithms are
proposed, in association with a protocol that permits simultaneous estimation,
on a shared set of frequencies, for each user equipment (UE), of the direction
of arrival and departure of the radio waves associated to the strongest
propagation paths from each of the surrounding access points (APs), so that
UE-AP association can take place. The proposed procedure relies on the
existence of a reliable control channel at sub-6 GHz frequency, so as to enable
exchange of estimated values between the UEs and the network, and assumes that
APs can be identifies based on the prior knowledge of the orthogonal channels
and transmit beamforming codebook. A strategy for assigning codebook entries to
the several APs is also proposed, with the aim of minimizing the mutual
interference between APs that are assigned the same entry. Numerical results
show the effectiveness of the proposed detection strategy, thus enabling one
shot fast BA for CF-mMIMO systems.


Abstract:
  A search with minimal model dependence for physics beyond the Standard Model
in events featuring three or four leptons ($3\ell$ and $4\ell$, $\ell = e,\mu$)
is presented. The analysis aims to be sensitive to a wide range of potential
new-physics theories simultaneously. This analysis uses data from $pp$
collisions delivered by the Large Hadron Collider at a centre-of-mass energy of
$\sqrt{s} = 13$ TeV and recorded with the ATLAS detector, corresponding to the
full Run 2 dataset of 139 fb$^{-1}$. The $3\ell$ and $4\ell$ phase space is
divided into 22 event categories according to the number of leptons in the
event, the missing transverse momentum, the invariant mass of the leptons, and
the presence of leptons originating from a $Z$-boson candidate. These event
categories are analysed independently for the presence of deviations from the
Standard Model. No statistically significant deviations from the Standard Model
predictions are observed. Upper limits for all signal regions are reported in
terms of the visible cross-section.


Abstract:
  The polarization-adjusted convolutional (PAC) codes concatenate the polar
transform and the convolutional transform to improve the decoding performance
of the finite-length polar codes, where the rate-profile is used to construct
the PAC codes by setting the positions of frozen bits. However, the optimal
rateprofile method of PAC codes is still unknown. In this paper, an optimized
rate-profile algorithm of PAC codes is proposed. First, we propose the
normalized compression factor (NCF) to quantify the transmission efficiency of
useful information, showing that the distribution of useful information that
needs to be transmitted after the convolutional transform should be adaptive to
the capacity profile after finite-length polar transform. This phenomenon
indicates that the PAC code improves the transmission efficiency of useful
information, which leads to a better decoding performance than the polar codes
with the same length. Then, we propose a novel rate-profile method of PAC
codes, where a quadratic optimization model is established and the Euclidean
norm of the NCF spectrum is adopted to construct the objective function.
Finally, a heuristic bit-swapping strategy is designed to search for the frozen
set with high objective function values, where the search space is limited by
considering the only bits with medium Hamming weight of the row index.
Simulation results show that the PAC codes with the proposed optimized
rate-profile construction have better decoding performance than the PAC codes
with the originally proposed Reed-Muller design construction.


Abstract:
  Data visualizations summarize high-dimensional distributions in two or three
dimensions. Dimensionality reduction entails a loss of information, and what is
preserved differs between methods. Existing methods preserve the local or the
global geometry of the points, and most techniques do not consider labels. Here
we introduce "hypersphere2sphere" (H2S), a new method that aims to visualize
not the points, but the relationships between the labeled distributions. H2S
fits a hypersphere to each labeled set of points in a high-dimensional space
and visualizes each hypersphere as a sphere in 3D (or circle in 2D). H2S
perfectly captures the geometry of up to 4 hyperspheres in 3D (or 3 in 2D), and
approximates the geometry for larger numbers of distributions, matching the
sizes (radii), and the pairwise separations (between-center distances) and
overlaps (along the center-connection line). The resulting visualizations are
robust to sampling imbalances. Leveraging labels and the sphere as the simplest
geometrical primitive, H2S provides an important addition to the toolbox of
visualization techniques.


Abstract:
  It is well-documented that word embeddings trained on large public corpora
consistently exhibit known human social biases. Although many methods for
debiasing exist, almost all fixate on completely eliminating biased information
from the embeddings and often diminish training set size in the process. In
this paper, we present a simple yet effective method for debiasing GloVe word
embeddings (Pennington et al., 2014) which works by incorporating explicit
information about training set bias rather than removing biased data outright.
Our method runs quickly and efficiently with the help of a fast bias gradient
approximation method from Brunet et al. (2019). As our approach is akin to the
notion of 'source criticism' in the humanities, we term our method
Source-Critical GloVe (SC-GloVe). We show that SC-GloVe reduces the effect size
on Word Embedding Association Test (WEAT) sets without sacrificing training
data or TOP-1 performance.


Abstract:
  This is a concise introduction to the topic of nonextensive Tsallis
statistics meant especially for those interested in its relation to high energy
proton-proton, proton-nucleus, and nucleus-nucleus collisions. The three types
of Tsallis statistics are reviewed. Only one of them is consistent with the
fundamental hypothesis of equilibrium statistical mechanics. The single
particle distributions associated with it, namely Boltzmann, Fermi-Dirac, and
Bose-Einstein, are derived. These are not equilibrium solutions to the
conventional Boltzmann transport equation which must be modified in a rather
nonintuitive manner for them to be so. Nevertheless the Boltzmann limit of the
Tsallis distribution is extremely efficient in representing a wide variety of
single particle distributions in high energy proton-proton, proton-nucleus, and
nucleus-nucleus collisions with only three parameters, one of them being the
so-called nonextensitivity parameter $q$. This distribution interpolates
between an exponential at low transverse energy, reflecting thermal
equilibrium, to a power law at high transverse energy, reflecting the
asymptotic freedom of QCD. It should not be viewed as a fundamental new
parameter representing nonextensive behavior in these collisions.


Abstract:
  Advances in isolating, controlling and entangling quantum systems are
transforming what was once a curious feature of quantum mechanics into a
vehicle for disruptive scientific and technological progress. Pursuing the
vision articulated by Feynman, a concerted effort across many areas of research
and development is introducing prototypical digital quantum devices into the
computing ecosystem available to domain scientists. Through interactions with
these early quantum devices, the abstract vision of exploring
classically-intractable quantum systems is evolving toward becoming a tangible
reality. Beyond catalyzing these technological advances, entanglement is
enabling parallel progress as a diagnostic for quantum correlations and as an
organizational tool, both guiding improved understanding of quantum many-body
systems and quantum field theories defining and emerging from the Standard
Model. From the perspective of three domain science theorists, this article
compiles thoughts about the interface on entanglement, complexity, and quantum
simulation in an effort to contextualize recent NISQ-era progress with the
scientific objectives of nuclear and high-energy physics.


Abstract:
  Many works in the recent literature introduce semantic mapping methods that
use CNNs (Convolutional Neural Networks) to recognize semantic properties in
images. The types of properties (eg.: room size, place category, and objects)
and their classes (eg.: kitchen and bathroom, for place category) are usually
predefined and restricted to a specific task. Thus, all the visual data
acquired and processed during the construction of the maps are lost and only
the recognized semantic properties remain on the maps. In contrast, this work
introduces a topological semantic mapping method that uses deep visual features
extracted by a CNN (GoogLeNet), from 2D images captured in multiple views of
the environment as the robot operates, to create, through averages,
consolidated representations of the visual features acquired in the regions
covered by each topological node. These representations allow flexible
recognition of semantic properties of the regions and use in other visual
tasks. Experiments with a real-world indoor dataset showed that the method is
able to consolidate the visual features of regions and use them to recognize
objects and place categories as semantic properties, and to indicate the
topological location of images, with very promising results.


Abstract:
  We present a new definition of non-ambiguous trees (NATs) as labelled binary
trees. We thus get a differential equation whose solution can be described
combinatorially. This yields a new formula for the number of NATs. We also
obtain q-versions of our formula. We finally generalise NATs to higher
dimension.


Abstract:
  Hexagonal chains are a special class of catacondensed benzenoid system and
phenylene chains are a class of polycyclic aromatic compounds. Recently, A
family of Sombor indices was introduced by Gutman in the chemical graph theory.
It had been examined that these indices may be successfully applied on modeling
thermodynamic properties of compounds. In this paper, we study the expected
values of the Sombor indices in random hexagonal chains, phenylene chains, and
consider the Sombor indices of some chemical graphs such as graphene, coronoid
systems and carbon nanocones.


Abstract:
  Explanations are used in recommender systems for various reasons. Users have
to be supported in making (high-quality) decisions more quickly. Developers of
recommender systems want to convince users to purchase specific items. Users
should better understand how the recommender system works and why a specific
item has been recommended. Users should also develop a more in-depth
understanding of the item domain. Consequently, explanations are designed in
order to achieve specific \emph{goals} such as increasing the transparency of a
recommendation or increasing a user's trust in the recommender system. In this
paper, we provide an overview of existing research related to explanations in
recommender systems, and specifically discuss aspects relevant to group
recommendation scenarios. In this context, we present different ways of
explaining and visualizing recommendations determined on the basis of
preference aggregation strategies.