Abstract:
  Magnetic field amplification by relativistic streaming plasma instabilities
is central to a wide variety of high-energy astrophysical environments as well
as to laboratory scenarios associated with intense lasers and electron beams.
We report on a new secondary nonlinear instability which arises for
relativistic dilute electron beams after the saturation of the linear Weibel
instability. This instability grows due to the transverse magnetic pressure
associated with the beam current filaments, which cannot be quickly neutralized
due to the inertia of background ions. We show that it can amplify the magnetic
field strength and spatial scale by orders of magnitude, leading to large-scale
plasma cavities with strong magnetic field and to very efficient conversion of
the beam kinetic energy into magnetic energy. The instability growth rate,
saturation level, and scale length are derived analytically and shown to be in
good agreement with fully-kinetic simulations.


Abstract:
  We introduce a novel data-driven framework for the design of targeted gene
panels for estimating exome-wide biomarkers in cancer immunotherapy. Our first
goal is to develop a generative model for the profile of mutation across the
exome, which allows for gene- and variant type-dependent mutation rates. Based
on this model, we then propose a new procedure for estimating biomarkers such
as Tumour Mutation Burden and Tumour Indel Burden. Our approach allows the
practitioner to select a targeted gene panel of a prespecified size, and then
construct an estimator that only depends on the selected genes. Alternatively,
the practitioner may apply our method to make predictions based on an existing
gene panel, or to augment a gene panel to a given size. We demonstrate the
excellent performance of our proposal using an annotated mutation dataset from
1144 Non-Small Cell Lung Cancer patients.


Abstract:
  Mechanically interlocked molecules have marked a breakthrough in the field of
topological chemistry and boosted the vigorous development of molecular
machinery. As an archetypal example of the interlocked molecules, catenanes
comprise macrocycles that are threaded through one another like links in a
chain. Inspired by the transition metal-templated approach of catenanes
synthesis, the hierarchical assembly of DNA origami catenanes templated by gold
nanoparticles is demonstrated in this work. DNA origami catenanes, which
contain two, three or four interlocked rings are successfully created. In
particular, the origami rings within the individual catenanes can be set free
with respect to one another by releasing the interconnecting gold
nanoparticles. This work will set the basis for rich progress toward DNA-based
molecular architectures with unique structural programmability and well-defined
topology.


Abstract:
  Speech enhancement algorithms based on deep learning have greatly surpassed
their traditional counterparts and are now being considered for the task of
removing acoustic echo from hands-free communication systems. This is a
challenging problem due to both real-world constraints like loudspeaker
non-linearities, and to limited compute capabilities in some communication
systems. In this work, we propose a system combining a traditional acoustic
echo canceller, and a low-complexity joint residual echo and noise suppressor
based on a hybrid signal processing/deep neural network (DSP/DNN) approach. We
show that the proposed system outperforms both traditional and other neural
approaches, while requiring only 5.5% CPU for real-time operation. We further
show that the system can scale to even lower complexity levels.


Abstract:
  Modern software systems rely on Deep Neural Networks (DNN) when processing
complex, unstructured inputs, such as images, videos, natural language texts or
audio signals. Provided the intractably large size of such input spaces, the
intrinsic limitations of learning algorithms, and the ambiguity about the
expected predictions for some of the inputs, not only there is no guarantee
that DNN's predictions are always correct, but rather developers must safely
assume a low, though not negligible, error probability. A fail-safe Deep
Learning based System (DLS) is one equipped to handle DNN faults by means of a
supervisor, capable of recognizing predictions that should not be trusted and
that should activate a healing procedure bringing the DLS to a safe state. In
this paper, we propose an approach to use DNN uncertainty estimators to
implement such a supervisor. We first discuss the advantages and disadvantages
of existing approaches to measure uncertainty for DNNs and propose novel
metrics for the empirical assessment of the supervisor that rely on such
approaches. We then describe our publicly available tool UNCERTAINTY-WIZARD,
which allows transparent estimation of uncertainty for regular tf.keras DNNs.
Lastly, we discuss a large-scale study conducted on four different subjects to
empirically validate the approach, reporting the lessons-learned as guidance
for software engineers who intend to monitor uncertainty for fail-safe
execution of DLS.


Abstract:
  The leading difficulty in achieving the contrast necessary to directly image
exoplanets and associated structures (eg. protoplanetary disks) at wavelengths
ranging from the visible to the infrared are quasi-static speckles, and they
are hard to distinguish from planets at the necessary level of precision. The
source of the quasi-static speckles is hardware aberrations that are not
compensated by the adaptive optics system. These aberrations are called
non-common path aberrations (NCPA). In 2013, Frazin showed how, in principle,
simultaneous millisecond (ms) telemetry from the wavefront sensor (WFS) and the
science camera behind a stellar coronagraph can be used as input into a
regression scheme that simultaneously and self-consistently estimates the NCPA
and the sought-after image of the planetary system (the exoplanet image). The
physical principle underlying the regression method is rather simple: the
wavefronts, which are measured by the WFS, modulate the speckles caused by the
NCPA and therefore can be used as probes of the optical system. The most
important departure from realism in the author's 2013 article was the
assumption that the WFS made error-free measurements. The simulations in Part I
provide results on the joint regression on the NCPA and the exoplanet image
from three different methods, called the ideal, the naive, and the
bias-corrected estimators. The ideal estimator is not physically realizable but
is a useful as a benchmark for simulation studies, but the other two are, at
least in principle. This article provides the regression equations for all
three of these estimators as well as a supporting technical discussion.
Briefly, the naive estimator simply uses the noisy WFS measurements without any
attempt to account for the errors, and the bias-corrected estimator uses
statistical knowledge of the wavefronts to treat errors in the WFS
measurements.


Abstract:
  As a result of the Hund's coupling, the band structure of the conducting
electrons in the skyrmion crystal (SkX) shares similar topological properties
with that of graphene, such as its cone-like shape, nonzero band Chern number,
edge states, and etc. In this work, we rigorously demonstrate that the Klein
tunneling phenomena is also shared by these two. We use the Green's function
technique and calculated the transmission probability of the electrons
tunneling through an electrostatic barrier in the SkX expressed by the double
exchange model. Numerical results of the SkX reproduced the Dirac model
obtained by linear fitting the two-dimensional band structure of the SkX.


Abstract:
  Goods trade is a supply chain transaction that involves shippers buying goods
from suppliers and carriers providing goods transportation. Shippers are issued
invoices from suppliers and carriers. Shippers carry out goods receiving and
invoice processing before payment processing of bills for suppliers and
carriers, where invoice processing includes tasks like processing claims and
adjusting the bill payments. Goods receiving involves verification of received
goods by the Shipper's receiving team. Invoice processing is carried out by the
Shipper's accounts payable team, which in turn is verified by the accounts
receivable teams of suppliers and carriers. This paper presents a
blockchain-based accounts payable system that generates claims for the
deficiency in the goods received and accordingly adjusts the payment in the
bills for suppliers and carriers. Primary motivations for these supply chain
organizations to adopt blockchain-based accounts payable systems are to
eliminate the process redundancies (accounts payable vs. accounts receivable),
to reduce the number of disputes among the transacting participants, and to
accelerate the accounts payable processes via optimizations in the claims
generation and blockchain-based dispute reconciliation.


Abstract:
  This study demonstrates the implementation of the stochastic ruler discrete
simulation optimization method for calibrating an agent-based model (ABM)
developed to simulate hepatitis C virus (HCV) transmission. The ABM simulates
HCV transmission between agents interacting in multiple environments relevant
for HCV transmission in the Indian context. Key outcomes of the ABM are HCV and
injecting drug user (IDU) prevalences among the simulated cohort. Certain input
parameters of the ABM need to be calibrated so that simulation outcomes attain
values as close as possible to real-world HCV and IDU prevalences. We
conceptualize the calibration process as a discrete simulation optimization
problem by discretizing the calibration parameter ranges, defining an
appropriate objective function, and then applying the stochastic ruler random
search method to solve this problem. We also present a method that exploits the
monotonic relationship between the simulation outcomes and calibration
parameters to yield improved calibration solutions with lesser computational
effort.


Abstract:
  We study the propagation of a specific class of instrumental systematics to
the reconstruction of the B-mode power spectrum of the cosmic microwave
background (CMB). We focus on the non-idealities of the half-wave plate (HWP),
a polarization modulator that is to be deployed by future CMB experiments, such
as the phase-A satellite mission LiteBIRD. We study the effects of non-ideal
HWP properties, such as transmittance, phase shift, and cross-polarization. To
this end, we developed a simple, yet stand-alone end-to-end simulation pipeline
adapted to LiteBIRD. We analyzed the effects of a possible mismatch between the
measured frequency profiles of HWP properties (used in the mapmaking stage of
the pipeline) and the actual profiles (used in the sky-scanning step). We
simulated single-frequency, CMB-only observations to emphasize the effects of
non-idealities on the BB power spectrum. We also considered multi-frequency
observations to account for the frequency dependence of HWP properties and the
contribution of foreground emission. We quantified the systematic effects in
terms of a bias $\Delta r$ on the tensor-to-scalar ratio, $r$, with respect to
the ideal case without systematic effects. We derived the accuracy requirements
on the measurements of HWP properties by requiring $\Delta r < 10^{-5}$ (1% of
the expected LiteBIRD sensitivity on $r$). Our analysis is introduced by a
detailed presentation of the mathematical formalism employed in this work,
including the use of the Jones and Mueller matrix representations.


Abstract:
  We describe the class of graphs for which all metric spaces with diametrical
graphs belonging to this class are ultrametric. It is shown that a metric space
$(X, d)$ is ultrametric iff the diametrical graph of the metric
$d_{\varepsilon}(x, y) = \max\{d(x, y), \varepsilon\}$ is either empty or
complete multipartite for every $\varepsilon > 0$. A refinement of the last
result is obtained for totally bounded spaces. Moreover, using complete
multipartite graphs we characterize the compact ultrametrizable topological
spaces. The bounded ultrametric spaces, which are weakly similar to unbounded
ones, are also characterized via complete multipartite graphs.


Abstract:
  Non-maximum Suppression (NMS) is an essential postprocessing step in modern
convolutional neural networks for object detection. Unlike convolutions which
are inherently parallel, the de-facto standard for NMS, namely GreedyNMS,
cannot be easily parallelized and thus could be the performance bottleneck in
convolutional object detection pipelines. MaxpoolNMS is introduced as a
parallelizable alternative to GreedyNMS, which in turn enables faster speed
than GreedyNMS at comparable accuracy. However, MaxpoolNMS is only capable of
replacing the GreedyNMS at the first stage of two-stage detectors like
Faster-RCNN. There is a significant drop in accuracy when applying MaxpoolNMS
at the final detection stage, due to the fact that MaxpoolNMS fails to
approximate GreedyNMS precisely in terms of bounding box selection. In this
paper, we propose a general, parallelizable and configurable approach
PSRR-MaxpoolNMS, to completely replace GreedyNMS at all stages in all
detectors. By introducing a simple Relationship Recovery module and a Pyramid
Shifted MaxpoolNMS module, our PSRR-MaxpoolNMS is able to approximate GreedyNMS
more precisely than MaxpoolNMS. Comprehensive experiments show that our
approach outperforms MaxpoolNMS by a large margin, and it is proven faster than
GreedyNMS with comparable accuracy. For the first time, PSRR-MaxpoolNMS
provides a fully parallelizable solution for customized hardware design, which
can be reused for accelerating NMS everywhere.


Abstract:
  We investigate two inequalities of Bugeaud and Laurent, each involving
triples of classical exponents of Diophantine approximation associated to
$\ux\in\mathbb{R}^n$. We provide a complete description of parameter triples
that admit equality for suitable $\ux$, which turns out rather surprising. For
$n=2$ our results agree with work of Laurent. Moreover, we establish lower
bounds for the Hausdorff and packing dimensions of the involved $\ux$, and in
special cases we can show they are sharp. Proofs are based on the variational
principle in parametric geometry of numbers, we enclose sketches of associated
combined graphs (templates) where equality is feasible. A twist of our
construction provides refined information on the joint spectrum of the
respective exponent triples.


Abstract:
  Let ${\mathcal A}$ be the class of functions that are analytic in the unit
disc ${\mathbb D}$, normalized such that $f(z)=z+\sum_{n=2}^\infty a_nz^n$, and
let class ${\mathcal U}(\lambda)$, $0<\lambda\le1$, consists of functions
$f\in{\mathcal A}$, such that \[ \left |\left (\frac{z}{f(z)} \right
)^{2}f'(z)-1\right | < \lambda\quad (z\in {\mathbb D}). \] In this paper we
determine the sharp upper bounds for the Hankel determinants of second and
third order for the inverse functions of functions from the class ${\mathcal
U}(\lambda)$.


Abstract:
  Laminar flow velocity profiles depend heavily on fluid rheology. Developing
methods of laminar flow characterization, based on low-field magnetic resonance
(MR), contributes to the widespread industrial application of the MR technique
in rheology. In this paper, we designed a low-cost, palm-sized permanent magnet
with a 1H resonance frequency of 20.48 MHz to measure laminar flow. The magnet
consists of two disk magnets, which were each tilted at an angle of 1{\deg}
from a starting separation of 1.4 cm to generate a constant gradient, 65
gauss/cm, in the direction of flow. Subsequently, a series of process methods,
for MR measurements, were proposed to characterize Newtonian and non-Newtonian
fluid flows in a pipe, including phase-based method, magnitude-based method,
and velocity spectrum method. The accuracies of the proposed methods were
validated by simulations, and experiments of Poiseuille flow and shear-thinning
flow on the designed magnet. The new velocity profile methods proposed are
advantageous because the MR instrumentation and measurement methods are simple
and portable. The sophistication is found in the analysis although the physical
principles are straight forward.


Abstract:
  Modern intelligent urban mobility applications are underpinned by
large-scale, multivariate, spatiotemporal data streams. Working with this data
presents unique challenges of data management, processing and presentation that
is often overlooked by researchers. Therefore, in this work we present an
integrated data management and processing framework for intelligent urban
mobility systems currently in use by our partner transit agencies. We discuss
the available data sources and outline our cloud-centric data management and
stream processing architecture built upon open-source publish-subscribe and
NoSQL data stores. We then describe our data-integrity monitoring methods. We
then present a set of visualization dashboards designed for our transit agency
partners. Lastly, we discuss how these tools are currently being used for
AI-driven urban mobility applications that use these tools.


Abstract:
  Most existing graph neural networks (GNNs) learn node embeddings using the
framework of message passing and aggregation. Such GNNs are incapable of
learning relative positions between graph nodes within a graph. To empower GNNs
with the awareness of node positions, some nodes are set as anchors. Then,
using the distances from a node to the anchors, GNNs can infer relative
positions between nodes. However, P-GNNs arbitrarily select anchors, leading to
compromising position-awareness and feature extraction. To eliminate this
compromise, we demonstrate that selecting evenly distributed and asymmetric
anchors is essential. On the other hand, we show that choosing anchors that can
aggregate embeddings of all the nodes within a graph is NP-hard. Therefore,
devising efficient optimal algorithms in a deterministic approach is
practically not feasible. To ensure position-awareness and bypass
NP-completeness, we propose Position-Sensing Graph Neural Networks (PSGNNs),
learning how to choose anchors in a back-propagatable fashion. Experiments
verify the effectiveness of PSGNNs against state-of-the-art GNNs, substantially
improving performance on various synthetic and real-world graph datasets while
enjoying stable scalability. Specifically, PSGNNs on average boost AUC more
than 14% for pairwise node classification and 18% for link prediction over the
existing state-of-the-art position-aware methods. Our source code is publicly
available at: https://github.com/ZhenyueQin/PSGNN


Abstract:
  Given that symbolic and ordinary powers of an ideal do not always coincide,
we look for conditions on the ideal such that equality holds for every natural
number. This paper focuses on studying the equality for Derksen ideals defined
by finite groups acting linearly on a polynomial ring.


Abstract:
  Recently many algorithms were devised for reinforcement learning (RL) with
function approximation. While they have clear algorithmic distinctions, they
also have many implementation differences that are algorithm-independent and
sometimes under-emphasized. Such mixing of algorithmic novelty and
implementation craftsmanship makes rigorous analyses of the sources of
performance improvements across algorithms difficult. In this work, we focus on
a series of off-policy inference-based actor-critic algorithms -- MPO, AWR, and
SAC -- to decouple their algorithmic innovations and implementation decisions.
We present unified derivations through a single control-as-inference objective,
where we can categorize each algorithm as based on either
Expectation-Maximization (EM) or direct Kullback-Leibler (KL) divergence
minimization and treat the rest of specifications as implementation details. We
performed extensive ablation studies, and identified substantial performance
drops whenever implementation details are mismatched for algorithmic choices.
These results show which implementation or code details are co-adapted and
co-evolved with algorithms, and which are transferable across algorithms: as
examples, we identified that tanh Gaussian policy and network sizes are highly
adapted to algorithmic types, while layer normalization and ELU are critical
for MPO's performances but also transfer to noticeable gains in SAC. We hope
our work can inspire future work to further demystify sources of performance
improvements across multiple algorithms and allow researchers to build on one
another's both algorithmic and implementational innovations.


Abstract:
  Understanding how attitudes towards the Climate Emergency vary can hold the
key to driving policy changes for effective action to mitigate climate related
risk. The Oil and Gas industry account for a significant proportion of global
emissions and so it could be speculated that there is a relationship between
Crude Oil Futures and sentiment towards the Climate Emergency. Using Latent
Dirichlet Allocation for Topic Modelling on a bespoke Twitter dataset, this
study shows that it is possible to split the conversation surrounding the
Climate Emergency into 3 distinct topics. Forecasting Crude Oil Futures using
Seasonal AutoRegressive Integrated Moving Average Modelling gives promising
results with a root mean squared error of 0.196 and 0.209 on the training and
testing data respectively. Understanding variation in attitudes towards climate
emergency provides inconclusive results which could be improved using
spatial-temporal analysis methods such as Density Based Clustering (DBSCAN).


Abstract:
  We explore the possibility of probing flavor violations in the charged-lepton
sector by means of high-luminosity lepton-photon and electron-muon collisions,
by inverting initial and final states in a variety of decay channels presently
used to bound such violations. In particular, we analyse the resonant lepton,
$\gamma\, \ell \to \ell^{\prime}$, and neutral-meson, $e^- \mu ^+ \to
\phi,\eta,\pi^0\!$, scattering channels, whose cross sections are critically
dependent on the colliding-beams energy spread, being particularly demanding in
the case of leptonic processes. For these processes, we compute upper bounds to
the cross-section corresponding to present limits on the inverse decay channel
rates. In order to circumvent the beam energy spread limitations we extend the
analysis to processes in which a photon accompanies the resonance in the final
state, compensating the off-shellness effects by radiative return. These
processes might be studied at future facilities with moderate energies, in case
lepton-photon and electron-muon collisions with sufficiently high luminosity
will be available.


Abstract:
  Deep generative models can synthesize photorealistic images of human faces
with novel identities. However, a key challenge to the wide applicability of
such techniques is to provide independent control over semantically meaningful
parameters: appearance, head pose, face shape, and facial expressions. In this
paper, we propose VariTex - to the best of our knowledge the first method that
learns a variational latent feature space of neural face textures, which allows
sampling of novel identities. We combine this generative model with a
parametric face model and gain explicit control over head pose and facial
expressions. To generate complete images of human heads, we propose an additive
decoder that adds plausible details such as hair. A novel training scheme
enforces a pose-independent latent space and in consequence, allows learning a
one-to-many mapping between latent codes and pose-conditioned exterior regions.
The resulting method can generate geometrically consistent images of novel
identities under fine-grained control over head pose, face shape, and facial
expressions. This facilitates a broad range of downstream tasks, like sampling
novel identities, changing the head pose, expression transfer, and more. Code
and models are available for research on https://mcbuehler.github.io/VariTex.


Abstract:
  Quadratic Unconstrained Binary Optimization (QUBO) is a general-purpose
modeling framework for combinatorial optimization problems and is a requirement
for quantum annealers. This paper utilizes the eigenvalue decomposition of the
underlying Q matrix to alter and improve the search process by extracting the
information from dominant eigenvalues and eigenvectors to implicitly guide the
search towards promising areas of the solution landscape. Computational results
on benchmark datasets illustrate the efficacy of our routine demonstrating
significant performance improvements on problems with dominant eigenvalues.


Abstract:
  We present a synthesis of fast radio burst (FRB) morphology (the change in
flux as a function of time and frequency) as detected in the 400-800 MHz octave
by the FRB project on the Canadian Hydrogen Intensity Mapping Experiment
(CHIME/FRB), using events from the first CHIME/FRB catalog. The catalog
consists of 61 bursts from 18 repeating sources, plus 474 one-off FRBs,
detected between 2018 July 25 and 2019 July 2. We identify four observed
archetypes of burst morphology ("simple broadband," "simple narrowband,"
"temporally complex" and "downward drifting") and describe relevant
instrumental biases that are essential for interpreting the observed
morphologies. Using the catalog properties of the FRBs, we confirm that bursts
from repeating sources, on average, have larger widths and we show, for the
first time, that bursts from repeating sources, on average, are narrower in
bandwidth. This difference could be due to a beaming or propagation effects, or
it could be intrinsic to the populations. We discuss potential implications of
these morphological differences for using FRBs as astrophysical tools.


Abstract:
  Influenced by the great success of deep learning in computer vision and
language understanding, research in recommendation has shifted to inventing new
recommender models based on neural networks. In recent years, we have witnessed
significant progress in developing neural recommender models, which generalize
and surpass traditional recommender models owing to the strong representation
power of neural networks. In this survey paper, we conduct a systematic review
on neural recommender models from the perspective of recommendation modeling
with the accuracy goal, aiming to summarize this field to facilitate
researchers and practitioners working on recommender systems. Specifically,
based on the data usage during recommendation modeling, we divide the work into
collaborative filtering and information-rich recommendation: 1) collaborative
filtering, which leverages the key source of user-item interaction data; 2)
content enriched recommendation, which additionally utilizes the side
information associated with users and items, like user profile and item
knowledge graph; and 3) temporal/sequential recommendation, which accounts for
the contextual information associated with an interaction, such as time,
location, and the past interactions. After reviewing representative work for
each type, we finally discuss some promising directions in this field.


Abstract:
  Given a graph $G = (V,E)$ with vertex weights $w(v)$ and a desired number of
parts $k$, the goal in graph partitioning problems is to partition the vertex
set V into parts $V_1,\ldots,V_k$. Metrics for compactness, contiguity, and
balance of the parts $V_i$ are frequent objectives, with much existing
literature focusing on compactness and balance. Revisiting an old method known
as striping, we give the first polynomial-time algorithms with guaranteed
contiguity and provable bicriteria approximations for compactness and balance
for planar grid graphs. We consider several types of graph partitioning,
including when vertex weights vary smoothly or are stochastic, reflecting
concerns in various real-world instances. We show significant improvements in
experiments for balancing workloads for the fire department and reducing
over-policing using 911 call data from South Fulton, GA.


Abstract:
  We study topological defect lines in two character rational conformal field
theories. Among them one set of two character theories are commutant pairs in
$E_{8,1}$ conformal field theory. Using these defect lines we construct defect
partition function in the $E_8$ theory. We find that the defects preserve only
a part of the $E_8$ current algebra symmetry. We also determine the defect
partition function in $c=24$ CFT using these defects lines of 2 character
theories and we find that these defects preserve all current algebra symmetries
of $c=24$ CFT.


Abstract:
  A common way to evaluate electronic integrals for polyatomic molecules is to
use Becke's partitioning scheme [J. Chem. Phys.88, 2547 (1988)] in conjunction
with overlapping grids centered at each atomic site. The Becke scheme was
designed for integrands that fall off rapidly at large distances, such as those
approximating bound electronic states. When applied to states in the electronic
continuum, however, Becke scheme exhibits slow convergence and it is highly
redundant. Here, we present a modified version of Becke scheme that is
applicable to functions of the electronic continuum, such as those involved in
molecular photoionization and electron-molecule scattering, and which ensures
convergence and efficiency comparable to those realized in the calculation of
bound states. In this modified scheme, the atomic weights already present in
Becke's partition are smoothly switched off within a range of few bond lengths
from their respective nuclei, and complemented by an asymptotically unitary
weight. The atomic integrals are evaluated on small spherical grids, centered
on each atom, with size commensurate to the support of the corresponding atomic
weight. The residual integral of the interstitial and long-range region is
evaluated with a central master grid. The accuracy of the method is
demonstrated by evaluating integrals involving integrands containing Gaussian
Type Orbitals and Yukawa potentials, on the atomic sites, as well as spherical
Bessel functions centered on the master grid. These functions are
representative of those encountered in realistic electron-scattering and
photoionization calculations in polyatomic molecules.


Abstract:
  Typical high quality text-to-speech (TTS) systems today use a two-stage
architecture, with a spectrum model stage that generates spectral frames and a
vocoder stage that generates the actual audio. High-quality spectrum models
usually incorporate the encoder-decoder architecture with self-attention or
bi-directional long short-term (BLSTM) units. While these models can produce
high quality speech, they often incur O($L$) increase in both latency and
real-time factor (RTF) with respect to input length $L$. In other words, longer
inputs leads to longer delay and slower synthesis speed, limiting its use in
real-time applications. In this paper, we propose a multi-rate attention
architecture that breaks the latency and RTF bottlenecks by computing a compact
representation during encoding and recurrently generating the attention vector
in a streaming manner during decoding. The proposed architecture achieves high
audio quality (MOS of 4.31 compared to groundtruth 4.48), low latency, and low
RTF at the same time. Meanwhile, both latency and RTF of the proposed system
stay constant regardless of input lengths, making it ideal for real-time
applications.


Abstract:
  Pre-trained text-to-text transformers such as BART have achieved impressive
performance across a range of NLP tasks. Recent study further shows that they
can learn to generalize to novel tasks, by including task descriptions as part
of the source sequence and training the model with (source, target) examples.
At test time, these fine-tuned models can make inferences on new tasks using
the new task descriptions as part of the input. However, this approach has
potential limitations, as the model learns to solve individual (source, target)
examples (i.e., at the instance level), instead of learning to solve tasks by
taking all examples within a task as a whole (i.e., at the task level). To this
end, we introduce Hypter, a framework that improves text-to-text transformer's
generalization ability to unseen tasks by training a hypernetwork to generate
task-specific, light-weight adapters from task descriptions. Experiments on
ZEST dataset and a synthetic SQuAD dataset demonstrate that Hypter improves
upon fine-tuning baselines. Notably, when using BART-Large as the main network,
Hypter brings 11.3% comparative improvement on ZEST dataset.


Abstract:
  In this letter, we propose a magnet-less non-reciprocal isolating system
based on time-varying metasurfaces. Two parallel time-varying metasurfaces, one
for frequency up-conversion and one for down-conversion by the same amount, are
used for realizing a region of space where incident waves from opposite
directions experience an opposite Doppler frequency shift. As a result, any
device within this region becomes sensitive to the illumination direction,
exhibiting a different scattering response from opposite directions and thus
breaking reciprocity. Very importantly, thanks to the opposite frequency shift
of the metasurfaces, the frequency of the transmitted electromagnetic field is
the same as for the incident one. Here, we demonstrate this general approach by
using a Bragg grating as the device between the time-varying metasurfaces. The
combined structure of the metasurfaces and the grating exhibits different
transmission and reflection properties for opposite illumination direction,
thereby realizing an isolator. More broadly, this letter presents a strategy
for converting any conventional electromagnetic device to a non-reciprocal one
by placing it between two time-varying metasurfaces. This approach opens the
door to several new non-reciprocal components based on thin and lightweight
metasurfaces, which are simpler to realize compared to their volumetric
counterparts.


Abstract:
  Following the Hu-Kriz method of computing the $C_2$ genuine dual Steenrod
algebra $(H\mathbf F_2)_{\bigstar}(H\mathbf F_2)$, we calculate the $C_4$
equivariant Bredon cohomology of the classifying space $\mathbf R P^{\infty
\rho}=B_{C_4}\Sigma_{2}$ as an $RO(C_4)$ graded Green-functor. We prove that as
a module over the homology of a point (which we also compute), this cohomology
is not flat. As a result, it can't be used as a test module for obtaining
generators in $(H\mathbf F_2)_{\bigstar}(H\mathbf F_2)$ as Hu-Kriz do in the
$C_2$ case.


Abstract:
  The paper introduces cycles cross ratio, which extends the classic cross
ratio of four points to various settings: conformal geometry, Lie spheres
geometry, etc. Just like its classic counterpart cycles cross ratio is a
measure of anharmonicity between spheres with respect to inversion. It also
provides a M\"obius invariant distance between spheres. Many further properties
of cycles cross ratio awaiting their exploration. In abstract framework the new
invariant can be considered in any projective space with a bilinear pairing.


Abstract:
  Self-testing results allow us to infer the underlying quantum mechanical
description of states and measurements from classical outputs produced by
non-communicating parties. The standard definition of self-testing does not
apply in situations when there are two or more inequivalent optimal strategies.
To address this, we introduce the notion of self-testing convex combinations of
reference strategies, which is a generalisation of self-testing to multiple
strategies. We show that the Glued Magic Square game [Quantum 4 (2020), p. 346]
self-tests a convex combination of two inequivalent strategies. As a corollary,
we obtain that the Glued Magic square game self-tests two EPR pairs thus
answering an open question from [Quantum 4 (2020), p. 346]. Our self-test is
robust and extends to natural generalisations of the Glued Magic Square game.


Abstract:
  Nowadays, live-stream and short video shopping in E-commerce have grown
exponentially. However, the sellers are required to manually match images of
the selling products to the timestamp of exhibition in the untrimmed video,
resulting in a complicated process. To solve the problem, we present an
innovative demonstration of multi-modal retrieval system called "Fashion
Focus", which enables to exactly localize the product images in the online
video as the focuses. Different modality contributes to the community
localization, including visual content, linguistic features and interaction
context are jointly investigated via presented multi-modal learning. Our system
employs two procedures for analysis, including video content structuring and
multi-modal retrieval, to automatically achieve accurate video-to-shop
matching. Fashion Focus presents a unified framework that can orientate the
consumers towards relevant product exhibitions during watching videos and help
the sellers to effectively deliver the products over search and recommendation.


Abstract:
  Human-computer interaction (HCI) is significantly impacted by delayed
responses from a spoken dialogue system. Hence, end-to-end (e2e) spoken
language understanding (SLU) solutions have recently been proposed to decrease
latency. Such approaches allow for the extraction of semantic information
directly from the speech signal, thus bypassing the need for a transcript from
an automatic speech recognition (ASR) system. In this paper, we propose a
compact e2e SLU architecture for streaming scenarios, where chunks of the
speech signal are processed continuously to predict intent and slot values. Our
model is based on a 3D convolutional neural network (3D-CNN) and a
unidirectional long short-term memory (LSTM). We compare the performance of two
alignment-free losses: the connectionist temporal classification (CTC) method
and its adapted version, namely connectionist temporal localization (CTL). The
latter performs not only the classification but also localization of sequential
audio events. The proposed solution is evaluated on the Fluent Speech Command
dataset and results show our model ability to process incoming speech signal,
reaching accuracy as high as 98.97 % for CTC and 98.78 % for CTL on
single-label classification, and as high as 95.69 % for CTC and 95.28 % for CTL
on two-label prediction.


Abstract:
  The so-called block-term decomposition (BTD) tensor model, especially in its
rank-$(L_r,L_r,1)$ version, has been recently receiving increasing attention
due to its enhanced ability of representing systems and signals that are
composed of \emph{block} components of rank higher than one, a scenario
encountered in numerous and diverse applications. Its uniqueness and
approximation have thus been thoroughly studied. The challenging problem of
estimating the BTD model structure, namely the number of block terms (rank) and
their individual (block) ranks, is of crucial importance in practice and has
only recently started to attract significant attention. In data-streaming
scenarios and/or big data applications, where the tensor dimension in one of
its modes grows in time or can only be processed incrementally, it is essential
to be able to perform model selection and computation in a recursive
(incremental/online) manner. To date there is only one such work in the
literature concerning the (general rank-$(L,M,N)$) BTD model, which proposes an
incremental method, however with the BTD rank and block ranks assumed to be
a-priori known and time invariant. In this preprint, a novel approach to
rank-$(L_r,L_r,1)$ BTD model selection and tracking is proposed, based on the
idea of imposing column sparsity jointly on the factors and estimating the
ranks as the numbers of factor columns of nonnegligible magnitude. An online
method of the alternating iteratively reweighted least squares (IRLS) type is
developed and shown to be computationally efficient and fast converging, also
allowing the model ranks to change in time. Its time and memory efficiency are
evaluated and favorably compared with those of the batch approach. Simulation
results are reported that demonstrate the effectiveness of the proposed scheme
in both selecting and tracking the correct BTD model.


Abstract:
  Phase I clinical trials are designed to test the safety (non-toxicity) of
drugs and find the maximum tolerated dose (MTD). This task becomes
significantly more challenging when multiple-drug dose-combinations (DC) are
involved, due to the inherent conflict between the exponentially increasing DC
candidates and the limited patient budget. This paper proposes a novel Bayesian
design, SDF-Bayes, for finding the MTD for drug combinations in the presence of
safety constraints. Rather than the conventional principle of escalating or
de-escalating the current dose of one drug (perhaps alternating between drugs),
SDF-Bayes proceeds by cautious optimism: it chooses the next DC that, on the
basis of current information, is most likely to be the MTD (optimism), subject
to the constraint that it only chooses DCs that have a high probability of
being safe (caution). We also propose an extension, SDF-Bayes-AR, that accounts
for patient heterogeneity and enables heterogeneous patient recruitment.
Extensive experiments based on both synthetic and real-world datasets
demonstrate the advantages of SDF-Bayes over state of the art DC trial designs
in terms of accuracy and safety.


Abstract:
  Let $R$ be a finitely generated positively graded algebra over a Noetherian
local ring $B$, and $\mathfrak{m} = [R]_+$ be the graded irrelevant ideal of
$R$. We provide a local criterion characterizing the $B$-freeness of all the
local cohomology modules $\text{H}_\mathfrak{m}^i(M)$ of a finitely generated
graded $R$-module $M$. We show that fiber-full modules are exactly the ones
that satisfy this criterion. When we change $B$ by an arbitrary Noetherian ring
$A$, we study the fiber-full locus of a module in $\text{Spec}(A)$: we show
that the fiber-full locus is always an open subset of $\text{Spec}(A)$ and that
it is dense when $A$ is generically reduced.


Abstract:
  I extend the calculations represented in \cite{konav} regarding the
resistivity in Kondo lattice materials from $3d$ syatem to $2d$ systems. In the
present work I consider a 2d system, and memory function is computed. However,
results found in 2d case are different from 3d system . I find that in $2d$ in
low temperature regime($ k_{B}T\ll \mu_d$) resistivity shows power
law($\frac{1}{T}$) behaviour and in the high temeprature regime($
k_{B}T\gg\mu_d$) resistivity varies linearly with temperature. In $3d$ these
behaviours are as $\frac{1}{T}$ and as $T^{\frac{3}{2}}$ respectively.


Abstract:
  In online domain-specific customer service applications, many companies
struggle to deploy advanced NLP models successfully, due to the limited
availability of and noise in their datasets. While prior research demonstrated
the potential of migrating large open-domain pretrained models for
domain-specific tasks, the appropriate (pre)training strategies have not yet
been rigorously evaluated in such social media customer service settings,
especially under multilingual conditions. We address this gap by collecting a
multilingual social media corpus containing customer service conversations
(865k tweets), comparing various pipelines of pretraining and finetuning
approaches, applying them on 5 different end tasks. We show that pretraining a
generic multilingual transformer model on our in-domain dataset, before
finetuning on specific end tasks, consistently boosts performance, especially
in non-English settings.


Abstract:
  Robust visualization of complex data is critical for the effective use of NLP
for event classification, as the volume of data is large and the
high-dimensional structure of text makes data challenging to summarize
succinctly. In event extraction tasks in particular, visualization can aid in
understanding and illustrating the textual relationships from which machine
learning tools produce insights. Through our case study which seeks to identify
potential triggers of state-led mass killings from news articles using NLP, we
demonstrate how visualizations can aid in each stage, from exploratory analysis
of raw data, to machine learning training analysis, and finally post-inference
validation.


Abstract:
  We identified 8 additional stars as members of the Helmi stream (HStr) in the
combined GALAH+ DR3 and $Gaia$ EDR3 catalog. By consistently reevaluating
claimed members from the literature, we consolidate a sample of 22 HStr stars
with parameters determined from high-resolution spectroscopy and spanning a
considerably wider (by $\sim$0.5 dex) metallicity interval ($-2.5 \lesssim
\rm[Fe/H] < -1.0$) than previously reported. Our study focuses on $\alpha$ (Mg
and Ca) and neutron-capture (Ba and Eu) elements. We find that the chemistry of
HStr is typical of dwarf spheroidal (dSph) galaxies, in good agreement with
previous $N$-body simulations of this merging event. Stars of HStr constitute a
clear declining sequence in $\rm[\alpha/Fe]$ for increasing metallicity up to
$\rm[Fe/H] \sim -1.0$. Moreover, stars of HStr show a median value of $+$0.5
dex for $\rm[Eu/Fe]$ with a small dispersion ($\pm$0.1 dex). Every star
analyzed with $\rm[Fe/H] < -1.2$ belong to the $r$-process enhanced
($\rm[Eu/Fe] > +0.3$ and $\rm[Ba/Eu] < 0.0$) metal-poor category, providing
remarkable evidence that, at such low-metallicity regime, stars of HStr
experienced enrichment in neutron-capture elements predominantly via
$r$-process nucleosynthesis. Finally, the extended metallicity range also
suggests an increase in $\rm[Ba/Eu]$ for higher $\rm[Fe/H]$, in conformity with
other surviving dwarf satellite galaxies of the Milky Way.


Abstract:
  This paper develops a Closed-Loop Error Learning Control (CLELC) algorithm
for feedback linearizable systems with experimental validation on a mobile
robot. Traditional feedback and feedforward controllers are designed based on
the nominal model by using Feedback Linearization Control (FLC) method. Then,
an intelligent controller is designed based on sliding mode learning algorithm
that utilizes closed-loop error dynamics to learn the system behavior. The
controllers are working in parallel, and the intelligent controller can
gradually replace the feedback controller from the control of the system. In
addition to the stability of the sliding mode learning algorithm, the
closed-loop stability of an $n$th order feedback linearizable system is proven.
The simulation results demonstrate that CLELC algorithm can improve control
performance (e.g., smaller rise time, settling time and overshoot) in the
absence of uncertainties, and also provides robust control performance in the
presence of uncertainties as compared to traditional FLC method. To test the
efficiency and efficacy of CLELC algorithm, the trajectory tracking problem of
a tracked mobile robot is studied in real-time. The experimental results
demonstrate that CLELC algorithm ensures high-accurate trajectory tracking
performance than traditional FLC method.


Abstract:
  Tyshkovskiy and Panchin have recently published a commentary on our paper in
which they outline several "points of disagreement with the Segreto/Deigin
hypothesis". As our paper is titled "The genetic structure of SARS-CoV-2 does
not rule out a laboratory origin", points of disagreement should provide
evidence that rules out a laboratory origin. However, Tyshkovskiy and Panchin
provide no such evidence and instead attempt to criticize our arguments that
highlight aspects of SARS-CoV-2 that could be consistent with the lab leak
hypothesis. Strikingly, Tyshkovskiy and Panchin's main point of criticism is
based on a false premise that we have claimed RaTG13 to be a direct progenitor
of SARS-CoV-2, and their other points of criticism are either incorrect or
irrelevant to our hypotheses. Thus, the genetic structure of SARS-CoV-2 remains
consistent with both natural or laboratory origin, which means that both the
zoonotic and the lab leak hypothesis need to be investigated equally
thoroughly.


Abstract:
  Scanning transmission electron microscopy (STEM) is the most widespread
adopted tool for atomic scale characterization of two-dimensional (2D)
materials. Many 2D materials remain susceptible to electron beam damage,
despite the standardized practice to reduce the beam energy from 200 keV to 80
or 60 keV. Although, all elements present can be detected by atomic
electrostatic potential imaging using integrated differential phase contrast
(iDPC) STEM or electron ptychography, capturing dynamics with atomic resolution
and enhanced sensitivity has remained a challenge. Here, by using iDPC-STEM, we
capture defect dynamics in 2D WS$_2$ by atomic electrostatic potential imaging
with a beam energy of only 30 keV. The direct imaging of atomic electrostatic
potentials with high framerate reveals the presence and motion of single atoms
near defects and edges in WS$_2$ that are otherwise invisible with conventional
annular dark-field STEM or cannot be captured sufficiently fast by electron
ptychography.


Abstract:
  Low dimensional ferroelectrics are highly desired for applications and full
of exotic physics. Here a functionalized MXene Hf$_2$CF$_2$ monolayer is
theoretically studied, which manifests a nonpolar to polar transition upon
moderate biaxial compressive strain. Accompanying this structural transition, a
metal-semiconductor transition occurs. The in-plane shift of unilateral
fluorine layer leads to a polarization pointing out-of-plane. Such
ferroelectricity is unconventional, similar to the recently-proposed
interlayer-sliding ferroelectricity but not identical. Due to its specific
hexapetalous potential energy profile, the possible ferroelectric switching
paths and domain walls are nontrivial, which are mediated via the metallic
paraelectric state. In this sense, the metallic walls can be manipulated by
reshaping the ferroelectric domains.


Abstract:
  In this article we study a particular class of compact connected orientable
PL $4$-manifolds with empty or connected boundary which have infinite cyclic
fundamental group. We show that the manifold in the class admits a handle
decomposition in which number of $2$-handles depends upon its second Betti
number and other $h$-handles ($h \leq 4$) are at most $2$. In particular, our
main result is that if $M$ is a closed connected orientable PL $4$-manifold
with fundamental group as $\mathbb{{Z}}$, then $M$ admits either of the
following handle decompositions:
  (1) one $0$-handle, two $1$-handles, $1+\beta_2(M)$ $2$-handles, one
$3$-handle and one $4$-handle,
  (2) one $0$-handle, one $1$-handle, $\beta_2(M)$ $2$-handles, one $3$-handle
and one $4$-handle, where $\beta_2(M)$ denotes the second Betti number of
manifold $M$ with $\mathbb{Z}$ coefficients. Further, we extend this result to
any compact connected orientable $4$-manifold $M$ with boundary and give three
possible representations of $M$ in terms of handles.


Abstract:
  Measurements of Higgs boson production cross sections and couplings in events
where the Higgs boson decays into a pair of photons are reported. Events are
selected from a sample of proton-proton collisions at $\sqrt{s} =$ 13 TeV
collected by the CMS detector at the LHC from 2016 to 2018, corresponding to an
integrated luminosity of 137 fb$^{-1}$. Analysis categories enriched in Higgs
boson events produced via gluon fusion, vector boson fusion, vector boson
associated production, and production associated with top quarks are
constructed. The total Higgs boson signal strength, relative to the standard
model (SM) prediction, is measured to be 1.12 $\pm$ 0.09. Other properties of
the Higgs boson are measured, including SM signal strength modifiers,
production cross sections, and its couplings to other particles. These include
the most precise measurements of gluon fusion and vector boson fusion Higgs
boson production in several different kinematic regions, the first measurement
of Higgs boson production in association with a top quark pair in five regions
of the Higgs boson transverse momentum, and an upper limit on the rate of Higgs
boson production in association with a single top quark. All results are found
to be in agreement with the SM expectations.


Abstract:
  Recent progress in pretraining language models on large corpora has resulted
in large performance gains on many NLP tasks. These large models acquire
linguistic knowledge during pretraining, which helps to improve performance on
downstream tasks via fine-tuning. To assess what kind of knowledge is acquired,
language models are commonly probed by querying them with `fill in the blank'
style cloze questions. Existing probing datasets mainly focus on knowledge
about relations between words and entities. We introduce WDLMPro (Word
Definition Language Model Probing) to evaluate word understanding directly
using dictionary definitions of words. In our experiments, three popular
pretrained language models struggle to match words and their definitions. This
indicates that they understand many words poorly and that our new probing task
is a difficult challenge that could help guide research on LMs in the future.


Abstract:
  In this paper, we study a new operation named pushforward on diffeological
vector pseudo-bundles, which is left adjoint to the pullback. We show how to
pushforward projective diffeological vector pseudo-bundles to get projective
diffeological vector spaces, producing many concrete new examples. This brings
new objects to diffeology from classical vector bundle theory.


Abstract:
  Semi-supervised learning (SSL) is an effective means to leverage unlabeled
data to improve a model's performance. Typical SSL methods like FixMatch assume
that labeled and unlabeled data share the same label space. However, in
practice, unlabeled data can contain categories unseen in the labeled set,
i.e., outliers, which can significantly harm the performance of SSL algorithms.
To address this problem, we propose a novel Open-set Semi-Supervised Learning
(OSSL) approach called OpenMatch. Learning representations of inliers while
rejecting outliers is essential for the success of OSSL. To this end, OpenMatch
unifies FixMatch with novelty detection based on one-vs-all (OVA) classifiers.
The OVA-classifier outputs the confidence score of a sample being an inlier,
providing a threshold to detect outliers. Another key contribution is an
open-set soft-consistency regularization loss, which enhances the smoothness of
the OVA-classifier with respect to input transformations and greatly improves
outlier detection. OpenMatch achieves state-of-the-art performance on three
datasets, and even outperforms a fully supervised model in detecting outliers
unseen in unlabeled data on CIFAR10.


Abstract:
  The competition between thermal fluctuations and potential forces is the
foundation of our understanding of phase transitions and matter in equilibrium.
Driving matter out of equilibrium allows for a new class of interactions which
are neither attractive nor repulsive but transverse. The existence of such
transverse forces immediately raises the question of how they interfere with
basic principles of material self-organization. Despite a recent surge of
interest, this question remains open. Here, we show that activating transverse
forces by homogeneous rotation of colloidal units generically turns otherwise
quiescent solids into a crystal whorl state dynamically shaped by
self-propelled dislocations. Simulations of both a minimal model and a full
hydrodynamics model establish the generic nature of the chaotic dynamics of
these self-kneading polycrystals. Using a continuum theory, we explain how odd
and Hall stresses conspire to destabilize chiral crystals from within. This
chiral instability produces dislocations that are unbound by their
self-propulsion. Their proliferation eventually leads to a crystalline whorl
state out of reach of equilibrium matter.


Abstract:
  We study stability of the sharp Poincar{\'e} constant of the invariant
probability measure of a reversible diffusion process satisfying some natural
conditions. The proof is based on the spectral interpretation of Poincar{\'e}
inequalities and Stein's method. In particular, these results are applied to
the gamma distributions and to strictly log-concave measures in dimension one,
giving stability for Brascamp-Lieb inequalities.


Abstract:
  In order to apply Optical Character Recognition (OCR) to historical printings
of Latin script fully automatically, we report on our efforts to construct a
widely-applicable polyfont recognition model yielding text with a Character
Error Rate (CER) around 2% when applied out-of-the-box. Moreover, we show how
this model can be further finetuned to specific classes of printings with
little manual and computational effort. The mixed or polyfont model is trained
on a wide variety of materials, in terms of age (from the 15th to the 19th
century), typography (various types of Fraktur and Antiqua), and languages
(among others, German, Latin, and French). To optimize the results we combined
established techniques of OCR training like pretraining, data augmentation, and
voting. In addition, we used various preprocessing methods to enrich the
training data and obtain more robust models. We also implemented a two-stage
approach which first trains on all available, considerably unbalanced data and
then refines the output by training on a selected more balanced subset.
Evaluations on 29 previously unseen books resulted in a CER of 1.73%,
outperforming a widely used standard model with a CER of 2.84% by almost 40%.
Training a more specialized model for some unseen Early Modern Latin books
starting from our mixed model led to a CER of 1.47%, an improvement of up to
50% compared to training from scratch and up to 30% compared to training from
the aforementioned standard model. Our new mixed model is made openly available
to the community.


Abstract:
  This paper presents a novel mechatronic setup intended for providing
respiratory support to patients suffering from pulmonary failure. The setup
relies upon the circulation of an oxygenated perfluorocarbon (PFC) through the
abdominal cavity. Such circulation provides a potential pathway for the
transport of oxygen to the bloodstream. However, the viability of this
technology for $CO_2$ clearance has not been established. Moreover, there is a
lack of experimental data enabling the modeling and identification of the
underlying dynamics of this technology. To address these gaps, we develop a
flexible experimental perfusion setup capable of monitoring and controlling key
variables such as perfusate flowrate, temperature, pressure, and oxygenation.
The paper (i) briefly summarizes the design of this setup; (ii) highlights the
degree to which its data acquisition system enables the collection and
cross-correlation of both perfusion-related and physiological variables; and
(iii) discusses the development of flow, pressure, and temperature control
algorithms for the setup. Experiments with large animals (swine) show that the
setup is capable of successfully controlling the perfusion process, as well as
gathering extensive data to support subsequent modeling and identification
studies.


Abstract:
  Yb-substituted Ni-Zn ferrites have been synthesized using sol-gel auto
combustion method. The structural characterization of the compositions has been
performed by X-ray diffraction analysis, field emission scanning electron
microscopy (FESEM), quantum design physical properties measurement system
(PPMS). That ensured the formation of single phase cubic spinel structure.
Crystallite and average grain size are calculated and found to decrease with
increasing Yb3+ contents. Saturation magnetization and Bohr magnetic moment
decrease while the coercivity increases with the increase in Yb3+ contents
successfully explained by the Neels collinear two sub-lattice model and
critical size effect, respectively. Critical particle size has been estimated
at 6.4 nm, the transition point between single domain regime (below the
critical size) and multi-domain regime (beyond the critical size). Curie
temperature reduces due to the weakening of A-O-B super exchange interaction
and redistribution of cations, confirmed by the M-T graph. The compositions
retain ferromagnetic ordered structured below Curie temperature and above Curie
temperature, it becomes paramagnetic, making them plausible candidates for high
temperature magnetic device applications. The relative quality factor peak is
obtained at a very high frequency, indicating the compositions could also be
applicable for high frequency magnetic device applications.


Abstract:
  Quasi-periodic plasmoid formation at the tip of magnetic streamer structures
is observed to occur in experiments on the Big Red Ball as well as in
simulations of these experiments performed with the extended-MHD code, NIMROD.
This plasmoid formation is found to occur on a characteristic timescale
dependent on pressure gradients and magnetic curvature in both experiment and
simulation. Single mode, or laminar, plasmoids exist when the pressure gradient
is modest, but give way to turbulent plasmoid ejection when the system drive is
higher, producing plasmoids of many sizes. However, a critical pressure
gradient is also observed, below which plasmoids are never formed. A simple
heuristic model of this plasmoid formation process is presented and suggested
to be a consequence of a dynamic loss of equilibrium in the high-$\beta$ region
of the helmet streamer. This model is capable of explaining the periodicity of
plasmoids observed in the experiment and simulations and produces plasmoid
periods of 90 minutes when applied to 2D models of solar streamers with a
height of $3R_\odot$. This is consistent with the location and frequency at
which periodic plasma blobs have been observed to form by LASCO and SECCHI
instruments.


Abstract:
  Maximum Entropy (MaxEnt) reinforcement learning is a powerful learning
paradigm which seeks to maximize return under entropy regularization. However,
action entropy does not necessarily coincide with state entropy, e.g., when
multiple actions produce the same transition. Instead, we propose to maximize
the transition entropy, i.e., the entropy of next states. We show that
transition entropy can be described by two terms; namely, model-dependent
transition entropy and action redundancy. Particularly, we explore the latter
in both deterministic and stochastic settings and develop tractable
approximation methods in a near model-free setup. We construct algorithms to
minimize action redundancy and demonstrate their effectiveness on a synthetic
environment with multiple redundant actions as well as contemporary benchmarks
in Atari and Mujoco. Our results suggest that action redundancy is a
fundamental problem in reinforcement learning.


Abstract:
  Enhanced room-temperature electromechanical coupling in the lead-free
ferroelectric system $(1-x)$BaZr$_{0.2}$Ti$_{0.8}$O$_{3}$ -
$x$Ba$_{0.7}$Ca$_{0.3}$TiO$_{3}$ (abbreviated as BZCT) at $x=0.5$ is attributed
to the existence of a morphotropic phase region (MPR) containing an
intermediate orthorhombic ($O$) phase between terminal rhombohedral ($R$) BZT
and tetragonal ($T$) BCT phases. However, there is ambiguity regarding the
morphotropic phase transition in BZCT at room temperature - while some
experiments suggest a single $O$ phase within the MPR, others indicate
coexistence of three polar phases ($T+R+O$). Therefore, to understand the
thermodynamic stability of polar phases and its relation to electromechanical
switching during morphotropic phase transition in BZCT, we develop a Landau
potential based on the theory of polar anisotropy. Since intrinsic
electrostrictive anisotropy changes as a function of electromechanical
processing, we establish a correlation between the parameters of our potential
and the coefficients of electrostriction. We also conducted phase-field
simulations based on this potential to demonstrate changes in domain
configuration from single-phase $O$ to three-phase $T+R+O$ at the equimolar
composition with the increase in electrostrictive anisotropy. Diffusionless
phase diagrams and the corresponding piezoelectric coefficients obtained from
our model compare well with the experimental findings. Increase in
electrostrictive anisotropy increases the degeneracy of the free energy at
ambient temperature and pressure leading to decreasing polar anisotropy,
although there is an accompanying increase in the electromechanical anisotropy
manifested by an increase in the difference between effective longitudinal and
transverse piezo-coefficients, $d_{33}$ and $d_{31}$.


Abstract:
  Clinical event sequences consist of thousands of clinical events that
represent records of patient care in time. Developing accurate prediction
models for such sequences is of a great importance for defining representations
of a patient state and for improving patient care. One important challenge of
learning a good predictive model of clinical sequences is patient-specific
variability. Based on underlying clinical complications, each patient's
sequence may consist of different sets of clinical events. However,
population-based models learned from such sequences may not accurately predict
patient-specific dynamics of event sequences. To address the problem, we
develop a new adaptive event sequence prediction framework that learns to
adjust its prediction for individual patients through an online model update.


Abstract:
  Fitting network models to neural activity is an important tool in
neuroscience. A popular approach is to model a brain area with a probabilistic
recurrent spiking network whose parameters maximize the likelihood of the
recorded activity. Although this is widely used, we show that the resulting
model does not produce realistic neural activity. To correct for this, we
suggest to augment the log-likelihood with terms that measure the dissimilarity
between simulated and recorded activity. This dissimilarity is defined via
summary statistics commonly used in neuroscience and the optimization is
efficient because it relies on back-propagation through the stochastically
simulated spike trains. We analyze this method theoretically and show
empirically that it generates more realistic activity statistics. We find that
it improves upon other fitting algorithms for spiking network models like GLMs
(Generalized Linear Models) which do not usually rely on back-propagation. This
new fitting algorithm also enables the consideration of hidden neurons which is
otherwise notoriously hard, and we show that it can be crucial when trying to
infer the network connectivity from spike recordings.


Abstract:
  It is known that phonons have angular momentum, and when the time-reversal
symmetry (TRS) is broken, the total phonon angular momentum in the whole system
becomes nonzero. In this paper, we propose that as an angular momentum of
phonons for a crystal without TRS, we need to consider the canonical angular
momentum, as opposed to the kinetic angular momentum in previous works. Next,
we show that the angular momentum of phonons without TRS exhibits universal
behaviors near the $\Gamma$ point. We focus on in-plane oscillations in
two-dimensional crystals as an example. By breaking the TRS, one of the
acoustic phonon branches at the $\Gamma$ point acquires a gap. We show that the
angular momentum of its acoustic phonon with a gap has a peak with the height
$\pm \hbar$ regardless of the details of the system. From this, we find that
this peak height changes discontinuously by changing the sign of the
TRS-breaking parameter.


Abstract:
  This paper describes a novel lossless compression method for point cloud
geometry, building on a recent lossy compression method that aimed at
reconstructing only the bounding volume of a point cloud. The proposed scheme
starts by partially reconstructing the geometry from the two depthmaps
associated to a single projection direction. The partial reconstruction
obtained from the depthmaps is completed to a full reconstruction of the point
cloud by sweeping section by section along one direction and encoding the
points which were not contained in the two depthmaps. The main ingredient is a
list-based encoding of the inner points (situated inside the feasible regions)
by a novel arithmetic three dimensional context coding procedure that
efficiently utilizes rotational invariances present in the input data.
State-of-the-art bits-per-voxel results are obtained on benchmark datasets.


Abstract:
  If Z is an open subscheme of Spec ZZ, X is a sufficiently nice Z-model of a
smooth curve over QQ, and p is a closed point of Z, the Chabauty-Kim method
leads to the construction of locally analytic functions on X(ZZ_p) which vanish
on X(Z); we call such functions "Kim functions". At least in broad outline, the
method generalizes readily to higher dimensions. In fact, in some sense, the
surface M_{0,5} should be easier than the previously studied curve M_{0,4}
since its points are closely related to those of M_{0,4}, yet they face a
further condition to integrality. This is mirrored by a certain "weight
advantage" we encounter, because of which, M_{0,5} possesses new Kim functions
not coming from M_{0,4}. Here we focus on the case "ZZ[1/6] in half-weight 4",
where we provide a first nontrivial example of a Kim function on a surface.
  Central to our approach to Chabauty-Kim theory (as developed in works by S.
Wewers, D. Corwin, and the first author) is the possibility of separating the
geometric part of the computation from its arithmetic context. However, we find
that in this case the geometric step grows beyond the bounds of standard
algorithms running on current computers. Therefore, some ingenuity is needed to
solve this seemingly straightforward problem, and our new Kim function is huge.


Abstract:
  X-ray pulse profile modeling of PSR J0740+6620, the most massive known
pulsar, with data from the NICER and XMM-Newton observatories recently led to a
measurement of its radius. We investigate this measurement's implications for
the neutron star equation of state (EoS), employing a nonparametric EoS model
based on Gaussian processes and combining information from other x-ray, radio
and gravitational-wave observations of neutron stars. Our analysis mildly
disfavors EoSs that support a disconnected hybrid star branch in the
mass-radius relation, a proxy for strong phase transitions, with a Bayes factor
of $6.9$. For such EoSs, the transition mass from the hadronic to the hybrid
branch is constrained to lie outside ($1,2$) $M_{\odot}$. We also find that the
conformal sound-speed bound is violated inside neutron star cores, which
implies that the core matter is strongly interacting. The squared sound speed
reaches a maximum of $0.75^{+0.25}_{-0.24}\, c^2$ at $3.60^{+2.25}_{-1.89}$
times nuclear saturation density at 90% credibility. Since all but the
gravitational-wave observations prefer a relatively stiff EoS, PSR J0740+6620's
central density is only $3.57^{+1.3}_{-1.3}$ times nuclear saturation, limiting
the density range probed by observations of cold, nonrotating neutron stars in
$\beta$-equilibrium.


Abstract:
  Matched filters are routinely used in cosmology in order to detect galaxy
clusters from mm observations through their thermal Sunyaev-Zeldovich (tSZ)
signature. In addition, they naturally provide an observable, the detection
signal-to-noise or significance, which can be used as a mass proxy in number
counts analyses of tSZ-selected cluster samples. In this work, we show that
this observable is, in general, non-Gaussian, and that it suffers from a
positive bias, which we refer to as optimisation bias. Both aspects arise from
the fact that the signal-to-noise is constructed through an optimisation
operation on noisy data, and hold even if the cluster signal is modelled
perfectly well, no foregrounds are present, and the noise is Gaussian. After
reviewing the general mathematical formalism underlying matched filters, we
study the statistics of the signal-to-noise with a set Monte Carlo mock
observations, finding it to be well-described by a unit-variance Gaussian for
signal-to-noise values of 6 and above, and quantify the magnitude of the
optimisation bias, for which we give an approximate expression that may be used
in practice. We also consider the impact of the bias on the cluster number
counts of Planck and the Simons Observatory (SO), finding it to be negligible
for the former and potentially significant for the latter.


Abstract:
  Self-propelled droplets are composed of droplets driven by the waves they
emit when bouncing on a vertically vibrated bath. Their dynamics is based on an
interplay between the waves and their source. The existence of self-spinning
modes is still controversial. Here, we show experimentally that these modes are
stable for a class of droplets and emerge spontaneously from noise
fluctuations. We perform a discrete stability analysis to confirm experimental
observations. In addition, we show that these self-spinning modes provide a
unique opportunity for a direct experimental measurement of parameters used in
the wave-driven droplet models found in the literature to enable comparison and
calibration.


Abstract:
  Artificial Neural Networks (ANNs) became popular due to their successful
application difficult problems such image and speech recognition. However, when
practitioners want to design an ANN they need to undergo laborious process of
selecting a set of parameters and topology. Currently, there are several
state-of-the art methods that allow for the automatic selection of some of
these aspects. Learning Rate optimizers are a set of such techniques that
search for good values of learning rates. Whilst these techniques are effective
and have yielded good results over the years, they are general solution i.e.
they do not consider the characteristics of a specific network.
  We propose a framework called AutoLR to automatically design Learning Rate
Optimizers. Two versions of the system are detailed. The first one, Dynamic
AutoLR, evolves static and dynamic learning rate optimizers based on the
current epoch and the previous learning rate. The second version, Adaptive
AutoLR, evolves adaptive optimizers that can fine tune the learning rate for
each network eeight which makes them generally more effective. The results are
competitive with the best state of the art methods, even outperforming them in
some scenarios. Furthermore, the system evolved a classifier, ADES, that
appears to be novel and innovative since, to the best of our knowledge, it has
a structure that differs from state of the art methods.


Abstract:
  We apply the Ionization Region Model (IRM) and the Orsay Boltzmann equation
for ELectrons coupled with Ionization and eXcited states kinetics (OBELIX)
model to study the electron kinetics of a high power impulse magnetron
sputtering (HiPIMS) discharge. In the IRM the bulk (cold) electrons are assumed
to exhibit a Maxwellian energy distribution and the secondary (hot) electrons,
emitted from the target surface upon ion bombardment, are treated as a high
energy tail, while in the OBELIX the electron energy distribution is calculated
self-consistently using an isotropic Boltzmann equation. The two models are
merged in the sense that the output from the IRM is used as an input for
OBELIX. The temporal evolutions of the particle densities are found to agree
very well between the two models. Furthermore, a very good agreement is
demonstrated between the bi-Maxwellian electron energy distribution assumed by
the IRM and the electron energy distribution calculated by the OBELIX model. It
can therefore be concluded that assuming a bi-Maxwellian electron energy
distribution, constituting a cold bulk electron group and a hot secondary
electron group, is a good approximation for modeling the HiPIMS discharge.


Abstract:
  We study the outflow dynamics and clogging phenomena of mixtures of soft,
elastic low-friction spherical grains and hard frictional spheres of similar
size in a quasi-two-dimensional (2D) silo with narrow orifice at the bottom.
Previous work has demonstrated the crucial influence of elasticity and friction
on silo discharge. We show that the addition of small amounts, even as low as
5\%, of hard grains to an ensemble of soft, low-friction grains already has
significant consequences. The mixtures allow a direct comparison of the
probabilities of the different types of particles to clog the orifice. We
analyze these probabilities for the hard, frictional and the soft, slippery
grains on the basis of their participation in the blocking arches, and compare
outflow velocities and durations of non-permanent clogs for different
compositions of the mixtures. Experimental results are compared with numerical
simulations. The latter strongly suggest a significant influence of the
inter-species particle friction.


Abstract:
  Isogeometric approach applied to Boundary Element Methods is an emerging
research area. In this context, the aim of the present contribution is that of
investigating, from a numerical point of view, the Symmetric Galerkin Boundary
Element Method (SGBEM) devoted to the solution of 2D boundary value problems
for the Laplace equation, where the boundary and the unknowns on it are both
represented by B-splines. We mainly compare this approach, which we call
IGA-SGBEM, with a curvilinear SGBEM, which operates on any boundary given by
explicit parametric representation and where the approximate solution is
obtained using Lagrangian basis. Both techniques are further compared with a
standard (conventional) SGBEM approach, where the boundary of the assigned
problem is approximated by linear elements and the numerical solution is
expressed in terms of Lagrangian basis. Several examples will be presented and
discussed, underlying benefits and drawbacks of all the above-mentioned
approaches.


Abstract:
  The modular decomposition of a symmetric map $\delta\colon X\times X \to
\Upsilon$ (or, equivalently, a set of symmetric binary relations, a
2-structure, or an edge-colored undirected graph) is a natural construction to
capture key features of $\delta$ in labeled trees. A map $\delta$ is explained
by a vertex-labeled rooted tree $(T,t)$ if the label $\delta(x,y)$ coincides
with the label of the last common ancestor of $x$ and $y$ in $T$, i.e., if
$\delta(x,y)=t(\mathrm{lca}(x,y))$. Only maps whose modular decomposition does
not contain prime nodes, i.e., the symbolic ultrametrics, can be exaplained in
this manner. Here we consider rooted median graphs as a generalization to
(modular decomposition) trees to explain symmetric maps. We first show that
every symmetric map can be explained by "extended" hypercubes and half-grids.
We then derive a a linear-time algorithm that stepwisely resolves prime
vertices in the modular decomposition tree to obtain a rooted and labeled
median graph that explains a given symmetric map $\delta$. We argue that the
resulting "tree-like" median graphs may be of use in phylogenetics as a model
of evolutionary relationships.


Abstract:
  Deep Metric Learning (DML) learns a non-linear semantic embedding from input
data that brings similar pairs together while keeps dissimilar data away from
each other. To this end, many different methods are proposed in the last decade
with promising results in various applications. The success of a DML algorithm
greatly depends on its loss function. However, no loss function is perfect, and
it deals only with some aspects of an optimal similarity embedding. Besides,
the generalizability of the DML on unseen categories during the test stage is
an important matter that is not considered by existing loss functions. To
address these challenges, we propose novel approaches to combine different
losses built on top of a shared deep feature extractor. The proposed ensemble
of losses enforces the deep model to extract features that are consistent with
all losses. Since the selected losses are diverse and each emphasizes different
aspects of an optimal semantic embedding, our effective combining methods yield
a considerable improvement over any individual loss and generalize well on
unseen categories. Here, there is no limitation in choosing loss functions, and
our methods can work with any set of existing ones. Besides, they can optimize
each loss function as well as its weight in an end-to-end paradigm with no need
to adjust any hyper-parameter. We evaluate our methods on some popular datasets
from the machine vision domain in conventional Zero-Shot-Learning (ZSL)
settings. The results are very encouraging and show that our methods outperform
all baseline losses by a large margin in all datasets.


Abstract:
  We use FIRE-2 simulations to examine 3-D variations of gas-phase elemental
abundances of [O/H], [Fe/H], and [N/H] in 11 Milky Way (MW) and M31-mass
galaxies across their formation histories at $z \leq 1.5$ ($t_{\rm lookback}
\leq 9.4$ Gyr), motivated by characterizing the initial conditions of stars for
chemical tagging. Gas within $1$ kpc of the disk midplane is vertically
homogeneous to $\lesssim 0.008$ dex at all $z \leq 1.5$. We find negative
radial gradients (metallicity decreases with galactocentric radius) at all
times, which steepen over time from $\approx -0.01$ dex kpc$^{-1}$ at $z = 1$
($t_{\rm lookback} = 7.8$ Gyr) to $\approx -0.03$ dex kpc$^{-1}$ at $z = 0$,
and which broadly agree with observations of the MW, M31, and nearby
MW/M31-mass galaxies. Azimuthal variations at fixed radius are typically $0.14$
dex at $z = 1$, reducing to $0.05$ dex at $z = 0$. Thus, over time radial
gradients become steeper while azimuthal variations become weaker (more
homogeneous). As a result, azimuthal variations were larger than radial
variations at $z \gtrsim 0.8$ ($t_{\rm lookback} \gtrsim 6.9$ Gyr).
Furthermore, elemental abundances are measurably homogeneous (to $\lesssim
0.05$ dex) across a radial range of $\Delta R \approx 3.5$ kpc at $z \gtrsim 1$
and $\Delta R \approx 1.7$ kpc at $z = 0$. We also measure full distributions
of elemental abundances, finding typically negatively skewed normal
distributions at $z \gtrsim 1$ that evolve to typically Gaussian distributions
by $z = 0$. Our results on gas abundances inform the initial conditions for
stars, including the spatial and temporal scales for applying chemical tagging
to understand stellar birth in the MW.


Abstract:
  We characterize mass, momentum, energy and metal outflow rates of multi-phase
galactic winds in a suite of FIRE-2 cosmological "zoom-in" simulations from the
Feedback in Realistic Environments (FIRE) project. We analyze simulations of
low-mass dwarfs, intermediate-mass dwarfs, Milky Way-mass halos, and
high-redshift massive halos. Consistent with previous work, we find that dwarfs
eject about 100 times more gas from their interstellar medium (ISM) than they
form in stars, while this mass "loading factor" drops below one in massive
galaxies. Most of the mass is carried by the hot phase ($>10^5$ K) in massive
halos and the warm phase ($10^3-10^5$ K) in dwarfs; cold outflows ($<10^3$ K)
are negligible except in high-redshift dwarfs. Energy, momentum and metal
loading factors from the ISM are of order unity in dwarfs and significantly
lower in more massive halos. Hot outflows have $2-5\times$ higher specific
energy than needed to escape from the gravitational potential of dwarf halos;
indeed, in dwarfs, the mass, momentum, and metal outflow rates increase with
radius whereas energy is roughly conserved, indicating swept up halo gas.
Burst-averaged mass loading factors tend to be larger during more powerful star
formation episodes and when the inner halo is not virialized, but we see
effectively no trend with the dense ISM gas fraction. We discuss how our
results can guide future controlled numerical experiments that aim to elucidate
the key parameters governing galactic winds and the resulting associated
preventative feedback.


Abstract:
  Controlling bias in training datasets is vital for ensuring equal treatment,
or parity, between different groups in downstream applications. A naive
solution is to transform the data so that it is statistically independent of
group membership, but this may throw away too much information when a
reasonable compromise between fairness and accuracy is desired. Another common
approach is to limit the ability of a particular adversary who seeks to
maximize parity. Unfortunately, representations produced by adversarial
approaches may still retain biases as their efficacy is tied to the complexity
of the adversary used during training. To this end, we theoretically establish
that by limiting the mutual information between representations and protected
attributes, we can assuredly control the parity of any downstream classifier.
We demonstrate an effective method for controlling parity through mutual
information based on contrastive information estimators and show that they
outperform approaches that rely on variational bounds based on complex
generative models. We test our approach on UCI Adult and Heritage Health
datasets and demonstrate that our approach provides more informative
representations across a range of desired parity thresholds while providing
strong theoretical guarantees on the parity of any downstream algorithm.


Abstract:
  In programming education, it makes a difference whether you are dealing with
beginners or advanced students. As our future students will become even more
tech-savvy, it is necessary to assess programming skills appropriately and
quickly to protect them from boredom and optimally support the learning
process. In this work, we advocate for the use of slice-based cohesion metrics
to assess the process of program construction in a learning analytics setting.
We argue that semantically related parts during program construction are an
essential part of programming skills. Therefore, we propose using cohesion
metrics on the level of variables to identify programmers' trains of thought
based on the cohesion of semantically related parts during program
construction.


Abstract:
  A logic is said to admit an equational completeness theorem when it can be
interpreted into the equational consequence relative to some class of algebras.
We characterize logics admitting an equational completeness theorem that are
either locally tabular or have some tautology. In particular, it is shown that
a protoalgebraic logic admits an equational completeness theorem precisely when
its has two distinct logically equivalent formulas. While the problem of
determining whether a logic admits an equational completeness theorem is shown
to be decidable both for logics presented by a finite set of finite matrices
and for locally tabular logics presented by a finite Hilbert calculus, it
becomes undecidable for arbitrary logics presented by finite Hilbert calculi.


Abstract:
  Efficient link configuration in millimeter wave (mmWave) communication
systems is a crucial yet challenging task due to the overhead imposed by beam
selection. For vehicle-to-infrastructure (V2I) networks, side information from
LIDAR sensors mounted on the vehicles has been leveraged to reduce the beam
search overhead. In this letter, we propose a federated LIDAR aided beam
selection method for V2I mmWave communication systems. In the proposed scheme,
connected vehicles collaborate to train a shared neural network (NN) on their
locally available LIDAR data during normal operation of the system. We also
propose a reduced-complexity convolutional NN (CNN) classifier architecture and
LIDAR preprocessing, which significantly outperforms previous works in terms of
both the performance and the complexity.


Abstract:
  Large-scale multi-agent cooperative control problems have materially enjoyed
the scalability, adaptivity, and flexibility of decentralized optimization.
However, due to the mandatory iterative communications between the agents and
the system operator, the decentralized architecture is vulnerable to malicious
attacks and privacy breach. Current research on addressing privacy preservation
of both agents and the system operator in cooperative decentralized
optimization with strongly coupled objective functions and constraints is still
primitive. To fill in the gaps, this paper proposes a novel privacy-preserving
decentralized optimization paradigm based on Paillier cryptosystem. The
proposed paradigm achieves ideal correctness and security, as well as resists
attacks from a range of adversaries. The efficacy and efficiency of the
proposed approach are verified via numerical simulations and a real-world
physical platform.


Abstract:
  A heterodimensional cycle is an invariant set of a dynamical system
consisting of two hyperbolic periodic orbits with different dimensions of their
unstable manifolds and a pair of orbits that connect them. For systems which
are at least $C^2$, we show that bifurcations of a coindex-1 heterodimensional
cycle within a generic 2-parameter family always create robust
heterodimensional dynamics, i.e., chain-transitive sets which contain
coexisting orbits with different numbers of positive Lyapunov exponents and
persist for an open set of parameter values. In particular, we solve the
so-called $C^r$-stabilization problem for the coindex-1 heterodimensional
cycles in any regularity class $r=2,\ldots,\infty,\omega$. The results are
based on the observation that arithmetic properties of moduli of topological
conjugacy of systems with heterodimensional cycles determine the emergence of
Bonatti-Diaz blenders.


Abstract:
  In this paper we introduce a discrete fractional resolvent family
$\{S_{\alpha,\beta}^n\}_{n\in\mathbb{N}_0}$ generated by a closed linear
operator in a Banach space $X$ for a given $\alpha,\beta>0.$ Moreover, we study
its main properties and, as a consequence, we obtain a method to study the
existence and uniqueness of the solutions to discrete fractional difference
equations in a Banach space.


Abstract:
  Virtual meetings are critical for remote work because of the need for
synchronous collaboration in the absence of in-person interactions. In-meeting
multitasking is closely linked to people's productivity and wellbeing. However,
we currently have limited understanding of multitasking in remote meetings and
its potential impact. In this paper, we present what we believe is the most
comprehensive study of remote meeting multitasking behavior through an analysis
of a large-scale telemetry dataset collected from February to May 2020 of U.S.
Microsoft employees and a 715-person diary study. Our results demonstrate that
intrinsic meeting characteristics such as size, length, time, and type,
significantly correlate with the extent to which people multitask, and
multitasking can lead to both positive and negative outcomes. Our findings
suggest important best-practice guidelines for remote meetings (e.g., avoid
important meetings in the morning) and design implications for productivity
tools (e.g., support positive remote multitasking).


Abstract:
  We present a comprehensive analysis of the potential sensitivity of the
Electron-Ion Collider (EIC) to charged lepton flavor violation (CLFV) in the
channel $ep\to \tau X$, within the model-independent framework of the Standard
Model Effective Field Theory (SMEFT). We compute the relevant cross sections to
leading order in QCD and electroweak corrections and perform simulations of
signal and SM background events in various $\tau$ decay channels, suggesting
simple cuts to enhance the associated estimated efficiencies. To assess the
discovery potential of the EIC in $\tau$-$e$ transitions, we study the
sensitivity of other probes of this physics across a broad range of energy
scales, from $pp \to e \tau X$ at the Large Hadron Collider to decays of $B$
mesons and $\tau$ leptons, such as $\tau \to e \gamma$, $\tau \to e \ell^+
\ell^-$, and crucially the hadronic modes $\tau \to e Y$ with $Y \in \{ \pi, K,
\pi \pi, K \pi, ...\}$. We find that electroweak dipole and four-fermion
semi-leptonic operators involving light quarks are already strongly constrained
by $\tau$ decays, while operators involving the $c$ and $b$ quarks present more
promising discovery potential for the EIC. An analysis of three models of
leptoquarks confirms the expectations based on the SMEFT results. We also
identify future directions needed to maximize the reach of the EIC in CLFV
searches: these include an optimization of the $\tau$ tagger in hadronic
channels, an exploration of background suppression through tagging $b$ and $c$
jets in the final state, and a global fit by turning on all SMEFT couplings,
which will likely reveal new discovery windows for the EIC.


Abstract:
  Word vector representations enable machines to encode human language for
spoken language understanding and processing. Confusion2vec, motivated from
human speech production and perception, is a word vector representation which
encodes ambiguities present in human spoken language in addition to semantics
and syntactic information. Confusion2vec provides a robust spoken language
representation by considering inherent human language ambiguities. In this
paper, we propose a novel word vector space estimation by unsupervised learning
on lattices output by an automatic speech recognition (ASR) system. We encode
each word in confusion2vec vector space by its constituent subword character
n-grams. We show the subword encoding helps better represent the acoustic
perceptual ambiguities in human spoken language via information modeled on
lattice structured ASR output. The usefulness of the proposed Confusion2vec
representation is evaluated using semantic, syntactic and acoustic analogy and
word similarity tasks. We also show the benefits of subword modeling for
acoustic ambiguity representation on the task of spoken language intent
detection. The results significantly outperform existing word vector
representations when evaluated on erroneous ASR outputs. We demonstrate that
Confusion2vec subword modeling eliminates the need for retraining/adapting the
natural language understanding models on ASR transcripts.


Abstract:
  This work proposes to use evolutionary computation as a pathway to allow a
new perspective on the modeling of energy expenditure and recovery of an
individual athlete during exercise. We revisit a theoretical concept called the
"three component hydraulic model" which is designed to simulate metabolic
systems during exercise and which is able to address recently highlighted
shortcomings of currently applied performance models. This hydraulic model has
not been entirely validated on individual athletes because it depends on
physiological measures that cannot be acquired in the required precision or
quantity. This paper introduces a generalized interpretation and formalization
of the three component hydraulic model that removes its ties to concrete
metabolic measures and allows to use evolutionary computation to fit its
parameters to an athlete.


Abstract:
  The Epoch of Reionisation (EoR) is the period within which the neutral
universe transitioned to an ionised one. This period remains unobserved using
low-frequency radio interferometers which target the 21 cm signal of neutral
hydrogen emitted in this era. The Murchison Widefield Array (MWA) radio
telescope was built with the detection of this signal as one of its major
science goals. One of the most significant challenges towards a successful
detection is that of calibration, especially in the presence of the Earth's
ionosphere. By introducing refractive source shifts, distorting source shapes
and scintillating flux densities, the ionosphere is a major nuisance in
low-frequency radio astronomy. We introduce SIVIO, a software tool developed
for simulating observations of the MWA through different ionospheric conditions
estimated using thin screen approximation models and propagated into the
visibilities. This enables us to directly assess the impact of the ionosphere
on observed EoR data and the resulting power spectra. We show that the
simulated data captures the dispersive behaviour of ionospheric effects. We
show that the spatial structure of the simulated ionospheric media is
accurately reconstructed either from the resultant source positional offsets or
from parameters evaluated during the data calibration procedure. In turn, this
will inform on the best strategies of identifying and efficiently eliminating
ionospheric contamination in EoR data moving into the Square Kilometre Array
era.


Abstract:
  This article presents a new hand architecture with three under-actuated
fingers. Each finger performs spatial movements to achieve more complex and
varied grasping than the existing planar-movement fingers. The purpose of this
hand is to grasp complex-shaped workpieces as they leave the machining centres.
Among the taxonomy of grips, cylindrical and spherical grips are often used to
grasp heavy objects. A combination of these two modes makes it possible to
capture most of the workpieces machined with 5-axis machines. However, the
change in grasping mode requires the fingers to reconfigure themselves to
perform spatial movements. This solution requires the addition of two or three
actuators to change the position of the fingers and requires sensors to
recognize the shape of the workpiece and determine the type of grasp to be
used. This article proposes to extend the notion of under-actuated fingers to
spatial movements. After a presentation of the kinematics of the fingers, the
problem of stability is discussed as well as the transmission of forces in this
mechanism. The complete approach for calculating the stability conditions is
presented from the study of Jacobian force transmission matrices. CAD
representations of the hand and its behavior in spherical and cylindrical grips
are presented.


Abstract:
  The main contribution of this manuscript is a local normal form for
Hamiltonian actions of Poisson-Lie groups $K$ on a symplectic manifold equipped
with an $AN$-valued moment map, where $AN$ is the dual Poisson-Lie group of
$K$. Our proof uses the delinearization theorem of Alekseev which relates a
classical Hamiltonian action of $K$ with $\mathfrak{k}^*$-valued moment map to
a Hamiltonian action with an $AN$-valued moment map, via a deformation of
symplectic structures. We obtain our main result by proving a ``delinearization
commutes with symplectic quotients'' theorem which is also of independent
interest, and then putting this together with the local normal form theorem for
classical Hamiltonian actions wtih $\mathfrak{k}^*$-valued moment maps. A key
ingredient for our main result is the delinearization
$\mathcal{D}(\omega_{can})$ of the canonical symplectic structure on $T^*K$, so
we additionally take some steps toward explicit computations of
$\mathcal{D}(\omega_{can})$. In particular, in the case $K=SU(2)$, we obtain
explicit formulas for the matrix coefficients of $\mathcal{D}(\omega_{can})$
with respect to a natural choice of coordinates on $T^*SU(2)$.