Abstract:
  We report parametric resonances (PRs) in the mean-field dynamics of a
one-dimensional dipolar Bose-Einstein condensate (DBEC) in widely varying
trapping geometries. The chief goal is to characterize the energy levels of
this system by analytical methods and the significance of this study arises
from the commonly known fact that in the presence of interactions the energy
levels of a trapped BEC are hard to calculate analytically. The latter
characterization is achieved by a matching of the PR energies to energy levels
of the confining trap using perturbative methods. Further, this work reveals
the role of the interplay between dipole-dipole interactions (DDI) and trapping
geometry in defining the energies and amplitudes of the PRs. The PRs are
induced by a negative Gaussian potential whose depth oscillates with time.
Moreover, the DDI play a role in this induction. The dynamics of this system is
modeled by the time-dependent Gross- Pitaevskii equation (TDGPE) that is
numerically solved by the Crank-Nicolson method. The PRs are discussed basing
on analytical methods: first, it is shown that it is possible to reproduce PRs
by the Lagrangian variational method that are similar to the ones obtained from
TDGPE. Second, the energies at which the PRs arise are closely matched with the
energy levels of the corresponding trap calculated by time-independent
perturbation theory. Third, the most probable transitions between the trap
energy levels yielding PRs are determined by time-dependent perturbation
theory. The most significant result of this work is that we have been able to
characterize the above mentioned energy levels of a DBEC in a complex trapping
potential.


Abstract:
  The digital transformation has been underway, creating digital shadows of
(almost) all physical entities and moving them to the Internet. The era of
Internet of Everything has therefore started to come into play, giving rise to
unprecedented traffic growths. In this context, optical core networks forming
the backbone of Internet infrastructure have been under critical issues of
reaching the capacity limit of conventional fiber, a phenomenon widely referred
as capacity crunch. For many years, the many-fold increases in fiber capacity
is thanks to exploiting physical dimensions for multiplexing optical signals
such as wavelength, polarization, time and lately space-division multiplexing
using multi-core fibers and such route seems to come to an end as almost all
known ways have been exploited. This necessitates for a departure from
traditional approaches to use the fiber capacity more efficiently and thereby
improve economics of scale. This paper lays out a new perspective to integrate
network coding (NC) functions into optical networks to achieve greater capacity
efficiency by upgrading intermediate nodes functionalities. In addition to the
review of recent proposals on new research problems enabled by NC operation in
optical networks, we also report state-of-the-art findings in the literature in
an effort to renew the interest of NC in optical networks and discuss three
critical points for pushing forward its applicability and practicality
including i) NC as a new dimension for multiplexing optical signals ii)
algorithmic aspects of NC-enabled optical networks design iii) NC as an
entirely fresh way for securing optical signals at physical layers


Abstract:
  In this paper, we propose a novel lightweight relation extraction approach of
structural block driven - convolutional neural learning. Specifically, we
detect the essential sequential tokens associated with entities through
dependency analysis, named as a structural block, and only encode the block on
a block-wise and an inter-block-wise representation, utilizing multi-scale
CNNs. This is to 1) eliminate the noisy from irrelevant part of a sentence;
meanwhile 2) enhance the relevant block representation with both block-wise and
inter-block-wise semantically enriched representation. Our method has the
advantage of being independent of long sentence context since we only encode
the sequential tokens within a block boundary. Experiments on two datasets
i.e., SemEval2010 and KBP37, demonstrate the significant advantages of our
method. In particular, we achieve the new state-of-the-art performance on the
KBP37 dataset; and comparable performance with the state-of-the-art on the
SemEval2010 dataset.


Abstract:
  A model investigating the role of geometry on the alpha dose rate of spent
nuclear fuel has been developed. This novel approach utilises a new piecewise
function to describe the probability of alpha escape as a function of
particulate radius, decay range within the material, and position from the
surface. The alpha dose rates were produced for particulates of radii 1 $\mu$m
to 10 mm, showing considerable changes in the 1 $\mu$m to 50 $\mu$m range.
Results indicate that for decreasing particulate sizes, approaching radii equal
to or less than the range of the $\alpha$-particle within the fuel, there is a
significant increase in the rate of energy emitted per unit mass of fuel
material. The influence of geometry is more significant for smaller radii,
showing clear differences in dose rate curves below 50 $\mu$m. These
considerations are essential for any future accurate prediction of the
dissolution rates and hydrogen gas release, driven by the radiolytic yields of
particulate spent nuclear fuel.


Abstract:
  We investigate the effectiveness of three different job-search and training
programmes for German long-term unemployed persons. On the basis of an
extensive administrative data set, we evaluated the effects of those programmes
on various levels of aggregation using Causal Machine Learning. We found
participants to benefit from the investigated programmes with placement
services to be most effective. Effects are realised quickly and are
long-lasting for any programme. While the effects are rather homogenous for
men, we found differential effects for women in various characteristics. Women
benefit in particular when local labour market conditions improve. Regarding
the allocation mechanism of the unemployed to the different programmes, we
found the observed allocation to be as effective as a random allocation.
Therefore, we propose data-driven rules for the allocation of the unemployed to
the respective labour market programmes that would improve the status-quo.


Abstract:
  Faceted summarization provides briefings of a document from different
perspectives. Readers can quickly comprehend the main points of a long document
with the help of a structured outline. However, little research has been
conducted on this subject, partially due to the lack of large-scale faceted
summarization datasets. In this study, we present FacetSum, a faceted
summarization benchmark built on Emerald journal articles, covering a diverse
range of domains. Different from traditional document-summary pairs, FacetSum
provides multiple summaries, each targeted at specific sections of a long
document, including the purpose, method, findings, and value. Analyses and
empirical results on our dataset reveal the importance of bringing structure
into summaries. We believe FacetSum will spur further advances in summarization
research and foster the development of NLP systems that can leverage the
structured information in both long texts and summaries.


Abstract:
  Considering a double-headed Brownian motor moving with both translational and
rotational degrees of freedom, we investigate the directed transport properties
of the system in a traveling-wave potential. It is found that the traveling
wave provides the essential condition of the directed transport for the system,
and at an appropriate angular frequency, the positive current can be optimized.
A general current reversal appears by modulating the angular frequency of the
traveling wave, noise intensity, external driving force and the rod length. By
transforming the dynamical equation in traveling-wave potential into that in a
tilted potential, the mechanism of current reversal is analyzed. For both cases
of Gaussian and Levy noises, the currents show similar dependence on the
parameters. Moreover, the current in the tilted potential shows a typical
stochastic resonance effect. The external driving force has also a
resonance-like effect on the current in the tilted potential. But the current
in the traveling-wave potential exhibits the reverse behaviors of that in the
tilted potential. Besides, the currents obviously depend on the stability index
of the Levy noise under certain conditions.


Abstract:
  We demonstrate size selective optical trapping and transport for
nanoparticles near an optical nanofiber taper. Using a two-wavelength,
counter-propagating mode configuration, we show that 100 nm diameter and 150 nm
diameter gold nanospheres (GNSs) are trapped by the evanescent field in the
taper region at different optical powers. Conversely, when one nanoparticle
species is trapped the other may be transported, leading to a sieve-like
effect. Our results show that sophisticated optical manipulation can be
achieved in a passive configuration by taking advantage of mode behavior in
nanophotonics devices.


Abstract:
  We investigated changes in the b value of the Gutenberg-Richter's law in and
around the focal areas of earthquakes on March 20 and on May 1, 2021, with
magnitude (M) 6.9 and 6.8, respectively, which occurred off the Pacific coast
of Miyagi prefecture, northeastern Japan. We showed that the b value in these
focal areas had been noticeably small, especially within a few years before the
occurrence of the M6.9 earthquake in its vicinity, indicating that differential
stress had been high in the focal areas. The coseismic slip of the 2011 Tohoku
earthquake seems to have stopped just short of the east side of the focus of
the M6.9 earthquake. Furthermore, the afterslip of the 2011 Tohoku earthquake
was relatively small in the focal areas of the M6.9 and M6.8 earthquakes,
compared to the surrounding regions. In addition, the focus of the M6.9
earthquake was situated close to the border point where the interplate slip in
the period from 2012 through 2021 has been considerably larger on the northern
side than on the southern side. The high-stress state inferred by the b-value
analysis is concordant with those characteristics of interplate slip events. We
found that the M6.8 earthquake on May 1 occurred near an area where the b value
remained small, even after the M6.9 quake. The ruptured areas by the two
earthquakes now seem to almost coincide with the small-b-value region that had
existed before their occurrence. The b value on the east side of the focal
areas of the M6.9 and M6.8 earthquakes which corresponds to the eastern part of
the source region of the 1978 off-Miyagi prefecture earthquake was consistently
large, while the seismicity enhanced by the two earthquakes also shows a large
b value, implying that stress in the region has not been very high.


Abstract:
  During the early history of unitary quantum theory the Kato's exceptional
points (EPs, a.k.a. non-Hermitian degeneracies) of Hamiltonians $H(\lambda)$
did not play any significant role, mainly due to the Stone theorem which firmly
connected the unitarity with the Hermiticity. During the recent wave of
optimism people started believing that the corridors of a unitary access to the
EPs could be opened leading, say, to a new picture of quantum phase transitions
via an {\it ad hoc} weakening of the Hermiticty (replaced by the
quasi-Hermiticity). Subsequently, the pessimism prevailed (the paths of access
appeared to be fragile). In a way restricted to the quantum physics of closed
systems a return to optimism is advocated here: the apparent fragility of the
corridors is claimed to follow from a misinterpretation of the theory in its
quasi-Hermitian formulation. Several perturbed versions of the realistic
many-body Bose-Hubbard model are chosen for illustration purposes.


Abstract:
  Existing works on visual counting primarily focus on one specific category at
a time, such as people, animals, and cells. In this paper, we are interested in
counting everything, that is to count objects from any category given only a
few annotated instances from that category. To this end, we pose counting as a
few-shot regression task. To tackle this task, we present a novel method that
takes a query image together with a few exemplar objects from the query image
and predicts a density map for the presence of all objects of interest in the
query image. We also present a novel adaptation strategy to adapt our network
to any novel visual category at test time, using only a few exemplar objects
from the novel category. We also introduce a dataset of 147 object categories
containing over 6000 images that are suitable for the few-shot counting task.
The images are annotated with two types of annotation, dots and bounding boxes,
and they can be used for developing few-shot counting models. Experiments on
this dataset shows that our method outperforms several state-of-the-art object
detectors and few-shot counting approaches. Our code and dataset can be found
at https://github.com/cvlab-stonybrook/LearningToCountEverything.


Abstract:
  Turbulent puffs are ubiquitous in everyday life phenomena. Understanding
their dynamics is important in a variety of situations ranging from industrial
processes to pure and applied science. In all these fields, a deep knowledge of
the statistical structure of temperature and velocity space/time fluctuations
is of paramount importance to construct models of chemical reaction (in
chemistry), of condensation of virus-containing droplets (in virology and/or
biophysics), and optimal mixing strategies in industrial applications. As a
matter of fact, results of turbulence in a puff are confined to bulk properties
(i.e. average puff velocity and typical decay/growth time) and dates back to
the second half of the 20th century. There is thus a huge gap to fill to pass
from bulk properties to two-point statistical observables. Here we fill this
gap exploiting theory and numerics in concert to predict and validate the
space/time scaling behaviors of both velocity and temperature structure
functions including intermittency corrections. Excellent agreement between
theory and simulations is found. Our results are expected to have profound
impact to develop evaporation models for virus-containing droplets carried by a
turbulent puff, with benefits to the comprehension of the airborne route of
virus contagion.


Abstract:
  The installation of electric vehicle charging stations (EVCS) will be
essential to promote the acceptance by the users of electric vehicles (EVs).
However, if EVCS are exclusively supplied by the grid, negative impacts on its
stability together with possible CO2 emission increases could be produced.
Introduction of hybrid renewable energy systems (HRES) for EVCS can cope with
both drawbacks by reducing the load on the grid and generating clean
electricity. This paper develops a methodology based on a weighted
multicriteria process to design the most suitable configuration for HRES in
EVCS. This methodology determines the local renewable resources and the EVCS
electricity demand. Then, taking into account environmental, economic and
technical aspects, it deduces the most adequate HRES design for the EVCS.
Besides, an experimental stage to validate the design deduced from the
multicriteria process is included. Therefore, the final design for the HRES in
EVCS is supported not only by a complete numerical evaluation, but also by an
experimental verification of the demand being fully covered. Methodology
application to Valencia (Spain) proves that an off-grid HRES with solar PV,
wind resources and batteries support would be the most suitable configuration
for the system. This solution was also experimentally verified.


Abstract:
  We use large deviation theory to obtain the free energy of the XY model on a
fully connected graph on each site of which there is a randomly oriented field
of magnitude $h$. The phase diagram is obtained for two symmetric distributions
of the random orientations: (a) a uniform distribution and (b) a distribution
with cubic symmetry. In both cases, the ordered state reflects the symmetry of
the underlying disorder distribution. The phase boundary has a multicritical
point which separates a locus of continuous transitions (for small values of
$h$) from a locus of first order transitions (for large $h$). The free energy
is a function of a single variable in case (a) and a function of two variables
in case (b), leading to different characters of the multicritical points in the
two cases.


Abstract:
  Machine learning (ML) tools such as encoder-decoder deep convolutional neural
networks (CNN) are able to extract relationships between inputs and outputs of
large complex systems directly from raw data. For time-varying systems the
predictive capabilities of ML tools degrade as the systems are no longer
accurately represented by the data sets with which the ML models were trained.
Re-training is possible, but only if the changes are slow and if new
input-output training data measurements can be made online non-invasively. In
this work we present an approach to deep learning for time-varying systems in
which adaptive feedback based only on available system output measurements is
applied to encoded low-dimensional dense layers of encoder-decoder type CNNs.
We demonstrate our method in developing an inverse model of a complex charged
particle accelerator system, mapping output beam measurements to input beam
distributions while both the accelerator components and the unknown input beam
distribution quickly vary with time. We demonstrate our results using
experimental measurements of the input and output beam distributions of the
HiRES ultra-fast electron diffraction (UED) microscopy beam line at Lawrence
Berkeley National Laboratory. We show how our method can be used to aid both
physics and ML-based surrogate online models to provide non-invasive beam
diagnostics and we also demonstrate how our method can be used to automatically
track the time varying quantum efficiency map of a particle accelerator's
photocathode.


Abstract:
  The extremely large magnetoresistance (XMR) effect in nonmagnetic semimetals
have attracted intensive attention recently. Here we propose an XMR candidate
material SrPd based on first-principles electronic structure calculations in
combination with a semi-classical model. The calculated carrier densities in
SrPd indicate that there is a good electron-hole compensation, while the
calculated intrinsic carrier mobilities are as high as 10$^5$
cm$^2$V$^{-1}$s$^{-1}$. There are only two doubly degenerate bands crossing the
Fermi level for SrPd, thus a semi-classical two-band model is available for
describing its transport properties. Accordingly, the magnetoresistance of SrPd
under a magnetic field of $4$ Tesla is predicted to reach ${10^5} \%$ at low
temperature. Furthermore, the calculated topological invariant indicates that
SrPd is topologically trivial. Our theoretical studies suggest that SrPd can
serve as an ideal platform to examine the charge compensation mechanism of the
XMR effect.


Abstract:
  Chemically peculiar stars in eclipsing binary systems are rare objects that
allow the derivation of fundamental stellar parameters and important
information on the evolutionary status and the origin of the observed chemical
peculiarities. Here we present an investigation of the known eclipsing binary
system BD+09 1467 = V680 Mon. Using spectra from the Large Sky Area
Multi-Object Fiber Spectroscopic Telescope (LAMOST) and own observations, we
identify the primary component of the system as a mercury-manganese (HgMn/CP3)
star (spectral type kB9 hB8 HeB9 V HgMn). Furthermore, photometric time series
data from the Transiting Exoplanet Survey Satellite (TESS) indicate that the
system is a "heartbeat star", a rare class of eccentric binary stars with
short-period orbits that exhibit a characteristic signature near the time of
periastron in their light curves due to the tidal distortion of the components.
Using all available photometric observations, we present an updated ephemeris
and binary system parameters as derived from modelling of the system with the
ELISa code, which indicates that the secondary star has an effective
temperature of Teff = 8300+-200 K (spectral type of about A4). V680 Mon is only
the fifth known eclipsing CP3 star and the first one in a heartbeat binary.
Furthermore, our results indicate that the star is located on the zero-age main
sequence and a possible member of the open cluster NGC 2264. As such, it lends
itself perfectly for detailed studies and may turn out to be a keystone in the
understanding of the development of CP3 star peculiarities.


Abstract:
  To achieve reliable mining results for massive vessel trajectories, one of
the most important challenges is how to efficiently compute the similarities
between different vessel trajectories. The computation of vessel trajectory
similarity has recently attracted increasing attention in the maritime data
mining research community. However, traditional shape- and warping-based
methods often suffer from several drawbacks such as high computational cost and
sensitivity to unwanted artifacts and non-uniform sampling rates, etc. To
eliminate these drawbacks, we propose an unsupervised learning method which
automatically extracts low-dimensional features through a convolutional
auto-encoder (CAE). In particular, we first generate the informative trajectory
images by remapping the raw vessel trajectories into two-dimensional matrices
while maintaining the spatio-temporal properties. Based on the massive vessel
trajectories collected, the CAE can learn the low-dimensional representations
of informative trajectory images in an unsupervised manner. The trajectory
similarity is finally equivalent to efficiently computing the similarities
between the learned low-dimensional features, which strongly correlate with the
raw vessel trajectories. Comprehensive experiments on realistic data sets have
demonstrated that the proposed method largely outperforms traditional
trajectory similarity computation methods in terms of efficiency and
effectiveness. The high-quality trajectory clustering performance could also be
guaranteed according to the CAE-based trajectory similarity computation
results.


Abstract:
  The gap generation in the dice model with local four-fermion interaction is
studied. Due to the presence of two valleys with degenerate electron states,
there are two main types of gaps. The intra- and intervalley gap describes the
electron and hole pairing in the same and different valleys, respectively. We
found that while the generation of the intravalley gap takes place only in the
supercritical regime, the intervalley gap is generated for an arbitrary small
coupling. The physical reason for the absence of the critical coupling is the
catalysis of the intervalley gap generation by the flat band in the electron
spectrum of the dice model. The completely quenched kinetic energy in the flat
band when integrated over momentum in the gap equation leads to extremely large
intervalley gap proportional to the area of the Brillouin zone.


Abstract:
  Automated defect inspection is critical for effective and efficient
maintenance, repair, and operations in advanced manufacturing. On the other
hand, automated defect inspection is often constrained by the lack of defect
samples, especially when we adopt deep neural networks for this task. This
paper presents Defect-GAN, an automated defect synthesis network that generates
realistic and diverse defect samples for training accurate and robust defect
inspection networks. Defect-GAN learns through defacement and restoration
processes, where the defacement generates defects on normal surface images
while the restoration removes defects to generate normal images. It employs a
novel compositional layer-based architecture for generating realistic defects
within various image backgrounds with different textures and appearances. It
can also mimic the stochastic variations of defects and offer flexible control
over the locations and categories of the generated defects within the image
background. Extensive experiments show that Defect-GAN is capable of
synthesizing various defects with superior diversity and fidelity. In addition,
the synthesized defect samples demonstrate their effectiveness in training
better defect inspection networks.


Abstract:
  Let $\mathcal{G}$ denote the variety generated by infinite dimensional
Grassmann algebras; i.e., the collection of all unitary associative algebras
satisfying the identity $[[z_1,z_2],z_3]=0$, where $[z_i,z_j]=z_iz_j-z_jz_i$.
Consider the free algebra $F_3$ in $\mathcal{G}$ generated by
$X_3=\{x_1,x_2,x_3\}$. The commutator ideal $F_3'$ of the algebra $F_3$ has a
natural $K[X_3]$-module structure. We call an element $p\in F_3$ symmetric if
$p(x_1,x_2,x_3)=p(x_{\xi1},x_{\xi2},x_{\xi3})$ for each permutation $\xi\in
S_3$. Symmetric elements form the subalgebra $F_3^{S_3}$ of invariants of the
symmetric group $S_3$ in $F_3$. We give a free generating set for the
$K[X_3]^{S_3}$-module $(F_3')^{S_3}$.


Abstract:
  We investigate the efficiency of two very different spoken term detection
approaches for transcription when the available data is insufficient to train a
robust ASR system. This work is grounded in very low-resource language
documentation scenario where only few minutes of recording have been
transcribed for a given language so far.Experiments on two oral languages show
that a pretrained universal phone recognizer, fine-tuned with only a few
minutes of target language speech, can be used for spoken term detection with a
better overall performance than a dynamic time warping approach. In addition,
we show that representing phoneme recognition ambiguity in a graph structure
can further boost the recall while maintaining high precision in the low
resource spoken term detection task.


Abstract:
  Multilingual Transformer-based language models, usually pretrained on more
than 100 languages, have been shown to achieve outstanding results in a wide
range of cross-lingual transfer tasks. However, it remains unknown whether the
optimization for different languages conditions the capacity of the models to
generalize over syntactic structures, and how languages with syntactic
phenomena of different complexity are affected. In this work, we explore the
syntactic generalization capabilities of the monolingual and multilingual
versions of BERT and RoBERTa. More specifically, we evaluate the syntactic
generalization potential of the models on English and Spanish tests, comparing
the syntactic abilities of monolingual and multilingual models on the same
language (English), and of multilingual models on two different languages
(English and Spanish). For English, we use the available SyntaxGym test suite;
for Spanish, we introduce SyntaxGymES, a novel ensemble of targeted syntactic
tests in Spanish, designed to evaluate the syntactic generalization
capabilities of language models through the SyntaxGym online platform.


Abstract:
  There has been much recent interest in two-sided markets and dynamics
thereof. In a rather a general discrete-time feedback model, which we show
conditions that assure that for each agent, there exists the limit of a
long-run average allocation of a resource to the agent, which is independent of
any initial conditions. We call this property the unique ergodicity.
  Our model encompasses two-sided markets and more complicated interconnections
of workers and customers, such as in a supply chain. It allows for
non-linearity of the response functions of market participants. Finally, it
allows for uncertainty in the response of market participants by considering a
set of the possible responses to either price or other signals and a measure to
sample from these.


Abstract:
  Social acceptability is an important consideration for HCI designers who
develop technologies for social contexts. However, the current theoretical
foundations of social acceptability research do not account for the complex
interactions among the actors in social situations and the specific role of
technology. In order to improve the understanding of how context shapes and is
shaped by situated technology interactions, we suggest to reframe the social
space as a dynamic bundle of social practices and explore it with simulation
studies using agent-based modeling. We outline possible research directions
that focus on specific interactions among practices as well as regularities in
emerging patterns.


Abstract:
  In a sharp contrast to the response of silica particles we show that the
metal-dielectric Janus particles with boojum defects in a nematic liquid
crystal are self-propelled under the action of an electric field applied
perpendicular to the director. The particles can be transported along any
direction in the plane of the sample by selecting the appropriate orientation
of the Janus vector with respect to the director. The direction of motion of
the particles is controllable by varying the field amplitude and frequency. The
command demonstrated on the motility of the particles is promising for tunable
transport and microrobotic applications.


Abstract:
  Sleep staging is fundamental for sleep assessment and disease diagnosis.
Although previous attempts to classify sleep stages have achieved high
classification performance, several challenges remain open: 1) How to
effectively extract salient waves in multimodal sleep data; 2) How to capture
the multi-scale transition rules among sleep stages; 3) How to adaptively seize
the key role of specific modality for sleep staging. To address these
challenges, we propose SalientSleepNet, a multimodal salient wave detection
network for sleep staging. Specifically, SalientSleepNet is a temporal fully
convolutional network based on the $\rm U^2$-Net architecture that is
originally proposed for salient object detection in computer vision. It is
mainly composed of two independent $\rm U^2$-like streams to extract the
salient features from multimodal data, respectively. Meanwhile, the multi-scale
extraction module is designed to capture multi-scale transition rules among
sleep stages. Besides, the multimodal attention module is proposed to
adaptively capture valuable information from multimodal data for the specific
sleep stage. Experiments on the two datasets demonstrate that SalientSleepNet
outperforms the state-of-the-art baselines. It is worth noting that this model
has the least amount of parameters compared with the existing deep neural
network models.


Abstract:
  Spin-phonon interaction is an important channel for spin and energy
relaxation in magnetic insulators. Understanding this interaction is critical
for developing magnetic insulator-based spintronic devices. Quantifying this
interaction in yttrium iron garnet (YIG), one of the most extensively
investigated magnetic insulators, remains challenging because of the large
number of atoms in a unit cell. Here, we report temperature-dependent and
polarization-resolved Raman measurements in a YIG bulk crystal. We first
classify the phonon modes based on their symmetry. We then develop a modified
mean-field theory and define a symmetry-adapted parameter to quantify
spin-phonon interaction in a phonon-mode specific way for the first time in
YIG. Based on this improved mean-field theory, we discover a positive
correlation between the spin-phonon interaction strength and the phonon
frequency.


Abstract:
  We propose a method for the unsupervised reconstruction of a
temporally-coherent sequence of surfaces from a sequence of time-evolving point
clouds, yielding dense, semantically meaningful correspondences between all
keyframes. We represent the reconstructed surface as an atlas, using a neural
network. Using canonical correspondences defined via the atlas, we encourage
the reconstruction to be as isometric as possible across frames, leading to
semantically-meaningful reconstruction. Through experiments and comparisons, we
empirically show that our method achieves results that exceed that state of the
art in the accuracy of unsupervised correspondences and accuracy of surface
reconstruction.


Abstract:
  Citrus segmentation is a key step of automatic citrus picking. While most
current image segmentation approaches achieve good segmentation results by
pixel-wise segmentation, these supervised learning-based methods require a
large amount of annotated data, and do not consider the continuous temporal
changes of citrus position in real-world applications. In this paper, we first
train a simple CNN with a small number of labelled citrus images in a
supervised manner, which can roughly predict the citrus location from each
frame. Then, we extend a state-of-the-art unsupervised learning approach to
pre-learn the citrus's potential movements between frames from unlabelled
citrus's videos. To take advantages of both networks, we employ the multimodal
transformer to combine supervised learned static information and unsupervised
learned movement information. The experimental results show that combing both
network allows the prediction accuracy reached at 88.3$\%$ IOU and 93.6$\%$
precision, outperforming the original supervised baseline 1.2$\%$ and 2.4$\%$.
Compared with most of the existing citrus segmentation methods, our method uses
a small amount of supervised data and a large number of unsupervised data,
while learning the pixel level location information and the temporal
information of citrus changes to enhance the segmentation effect.


Abstract:
  A bipartite experiment consists of one set of units being assigned treatments
and another set of units for which we measure outcomes. The two sets of units
are connected by a bipartite graph, governing how the treated units can affect
the outcome units. In this paper, we consider estimation of the average total
treatment effect in the bipartite experimental framework under a linear
exposure-response model. We introduce the Exposure Reweighted Linear (ERL)
estimator, and show that the estimator is unbiased, consistent and
asymptotically normal, provided that the bipartite graph is sufficiently
sparse. To facilitate inference, we introduce an unbiased and consistent
estimator of the variance of the ERL point estimator. In addition, we introduce
a cluster-based design, Exposure-Design, that uses heuristics to increase the
precision of the ERL estimator by realizing a desirable exposure distribution.


Abstract:
  Given a stream of graph edges from a dynamic graph, how can we assign anomaly
scores to edges and subgraphs in an online manner, for the purpose of detecting
unusual behavior, using constant time and memory? For example, in intrusion
detection, existing work seeks to detect either anomalous edges or anomalous
subgraphs, but not both. In this paper, we first extend the count-min sketch
data structure to a higher-order sketch. This higher-order sketch has the
useful property of preserving the dense subgraph structure (dense subgraphs in
the input turn into dense submatrices in the data structure). We then propose
four online algorithms that utilize this enhanced data structure, which (a)
detect both edge and graph anomalies; (b) process each edge and graph in
constant memory and constant update time per newly arriving edge, and; (c)
outperform state-of-the-art baselines on four real-world datasets. Our method
is the first streaming approach that incorporates dense subgraph search to
detect graph anomalies in constant memory and time.


Abstract:
  Effectively modeling phenomena present in highly nonlinear dynamical systems
whilst also accurately quantifying uncertainty is a challenging task, which
often requires problem-specific techniques. We present a novel, domain-agnostic
approach to tackling this problem, using compositions of physics-informed
random features, derived from ordinary differential equations. The architecture
of our model leverages recent advances in approximate inference for deep
Gaussian processes, such as layer-wise weight-space approximations which allow
us to incorporate random Fourier features, and stochastic variational inference
for approximate Bayesian inference. We provide evidence that our model is
capable of capturing highly nonlinear behaviour in real-world multivariate time
series data. In addition, we find that our approach achieves comparable
performance to a number of other probabilistic models on benchmark regression
tasks.


Abstract:
  Laser-induced ultrafast demagnetization has puzzled researchers around the
world for over two decades. Intrinsic complexity in electronic, magnetic, and
phononic subsystems is difficult to understand microscopically. So far it is
not possible to explain demagnetization using a single mechanism, which
suggests a crucial piece of information still missing. In this paper, we return
to a fundamental aspect of physics: spin and its change within each band in the
entire Brillouin zone. We employ fcc Ni as an example and use an extremely
dense {\bf k} mesh to map out spin changes for every band close to the Fermi
level along all the high symmetry lines. To our surprise, spin angular momentum
at some special {\bf k} points abruptly changes from $\pm \hbar/2$ to $\mp
\hbar/2$ simply by moving from one crystal momentum point to the next. This
explains why intraband transitions, which the spin superdiffusion model is
based upon, can induce a sharp spin moment reduction, and why electric current
can change spin orientation in spintronics. These special {\bf k} points, which
are called spin Berry points, are not random and appear when several bands are
close to each other, so the Berry potential of spin majority states is
different from that of spin minority states. Although within a single band,
spin Berry points jump, when we group several neighboring bands together, they
form distinctive smooth spin Berry lines. It is the band structure that
disrupts those lines. Spin Berry points are crucial to laser-induced ultrafast
demagnetization and spintronics.


Abstract:
  Surface-response functions are one of the most promising routes for bridging
the gap between fully quantum-mechanical calculations and phenomenological
models in quantum nanoplasmonics. Within all the currently available recipes
for obtaining such response functions, \emph{ab initio} calculations remain one
of the most predominant, wherein the surface-response function are retrieved
via the metal's non-equilibrium response to an external perturbation. Here, we
present a complementary approach where one of the most appealing
surface-response functions, namely the Feibelman $d$-parameters, yield a finite
contribution even in the case where they are calculated directly from the
equilibrium properties described under the local-response approximation (LRA),
but with a spatially varying equilibrium electron density. Using model
calculations that mimic both spill-in and spill-out of the equilibrium electron
density, we show that the obtained $d$-parameters are in qualitative agreement
with more elaborate, but also more computationally demanding, \emph{ab initio}
methods. The analytical work presented here illustrates how microscopic
surface-response functions can emerge out of entirely local electrodynamic
considerations.


Abstract:
  In this paper, a novel and robust algorithm is proposed for adaptive
beamforming based on the idea of reconstructing the autocorrelation sequence
(ACS) of a random process from a set of measured data. This is obtained from
the first column and the first row of the sample covariance matrix (SCM) after
averaging along its diagonals. Then, the power spectrum of the correlation
sequence is estimated using the discrete Fourier transform (DFT). The DFT
coefficients corresponding to the angles within the noise-plus-interference
region are used to reconstruct the noise-plus-interference covariance matrix
(NPICM), while the desired signal covariance matrix (DSCM) is estimated by
identifying and removing the noise-plus-interference component from the SCM. In
particular, the spatial power spectrum of the estimated received signal is
utilized to compute the correlation sequence corresponding to the
noise-plus-interference in which the dominant DFT coefficient of the
noise-plus-interference is captured. A key advantage of the proposed adaptive
beamforming is that only little prior information is required. Specifically, an
imprecise knowledge of the array geometry and of the angular sectors in which
the interferences are located is needed. Simulation results demonstrate that
compared with previous reconstruction-based beamformers, the proposed approach
can achieve better overall performance in the case of multiple mismatches over
a very large range of input signal-to-noise ratios.


Abstract:
  Neural models have transformed the fundamental information retrieval problem
of mapping a query to a giant set of items. However, the need for efficient and
low latency inference forces the community to reconsider efficient approximate
near-neighbor search in the item space. To this end, learning to index is
gaining much interest in recent times. Methods have to trade between obtaining
high accuracy while maintaining load balance and scalability in distributed
settings. We propose a novel approach called IRLI (pronounced `early'), which
iteratively partitions the items by learning the relevant buckets directly from
the query-item relevance data. Furthermore, IRLI employs a superior
power-of-$k$-choices based load balancing strategy. We mathematically show that
IRLI retrieves the correct item with high probability under very natural
assumptions and provides superior load balancing. IRLI surpasses the best
baseline's precision on multi-label classification while being $5x$ faster on
inference. For near-neighbor search tasks, the same method outperforms the
state-of-the-art Learned Hashing approach NeuralLSH by requiring only ~
{1/6}^th of the candidates for the same recall. IRLI is both data and model
parallel, making it ideal for distributed GPU implementation. We demonstrate
this advantage by indexing 100 million dense vectors and surpassing the popular
FAISS library by >10% on recall.


Abstract:
  The magnetic ground state of polycrystalline N\'eel skyrmion hosting material
GaV$_4$S$_8$ has been investigated using ac susceptibility and powder neutron
diffraction. In the absence of an applied magnetic field GaV$_4$S$_8$ undergoes
a transition from a paramagnetic to a cycloidal state below 13~K and then to a
ferromagnetic-like state below 6~K. With evidence from ac susceptibility and
powder neutron diffraction, we have identified the commensurate magnetic
structure at 1.5 K, with ordered magnetic moments of $0.23(2)~\mu_{\mathrm{B}}$
on the V1 sites and $0.22(1)~\mu_{\mathrm{B}}$ on the V2 sites. These moments
have ferromagnetic-like alignment but with a 39(8)$^{\circ}$ canting of the
magnetic moments on the V2 sites away from the V$_4$ cluster. In the
incommensurate magnetic phase that exists between 6 and 13 K, we provide a
thorough and careful analysis of the cycloidal magnetic structure exhibited by
this material using powder neutron diffraction.


Abstract:
  Deep learning has advanced from fully connected architectures to structured
models organized into components, e.g., the transformer composed of positional
elements, modular architectures divided into slots, and graph neural nets made
up of nodes. In structured models, an interesting question is how to conduct
dynamic and possibly sparse communication among the separate components. Here,
we explore the hypothesis that restricting the transmitted information among
components to discrete representations is a beneficial bottleneck. The
motivating intuition is human language in which communication occurs through
discrete symbols. Even though individuals have different understandings of what
a "cat" is based on their specific experiences, the shared discrete token makes
it possible for communication among individuals to be unimpeded by individual
differences in internal representation. To discretize the values of concepts
dynamically communicated among specialist components, we extend the
quantization mechanism from the Vector-Quantized Variational Autoencoder to
multi-headed discretization with shared codebooks and use it for
discrete-valued neural communication (DVNC). Our experiments show that DVNC
substantially improves systematic generalization in a variety of architectures
-- transformers, modular architectures, and graph neural networks. We also show
that the DVNC is robust to the choice of hyperparameters, making the method
very useful in practice. Moreover, we establish a theoretical justification of
our discretization process, proving that it has the ability to increase noise
robustness and reduce the underlying dimensionality of the model.


Abstract:
  Optimal stopping is the problem of deciding the right time at which to take a
particular action in a stochastic system, in order to maximize an expected
reward. It has many applications in areas such as finance, healthcare, and
statistics. In this paper, we employ deep Reinforcement Learning (RL) to learn
optimal stopping policies in two financial engineering applications: namely
option pricing, and optimal option exercise. We present for the first time a
comprehensive empirical evaluation of the quality of optimal stopping policies
identified by three state of the art deep RL algorithms: double deep Q-learning
(DDQN), categorical distributional RL (C51), and Implicit Quantile Networks
(IQN). In the case of option pricing, our findings indicate that in a
theoretical Black-Schole environment, IQN successfully identifies nearly
optimal prices. On the other hand, it is slightly outperformed by C51 when
confronted to real stock data movements in a put option exercise problem that
involves assets from the S&P500 index. More importantly, the C51 algorithm is
able to identify an optimal stopping policy that achieves 8% more out-of-sample
returns than the best of four natural benchmark policies. We conclude with a
discussion of our findings which should pave the way for relevant future
research.


Abstract:
  We present the sympathetic eruption of a standard and a blowout coronal jets
originating from two adjacent coronal bright points (CBP1 and CBP2) in a polar
coronal hole, using soft X-ray and extreme ultraviolet observations
respectively taken by the Hinode and the Solar Dynamic Observatory. In the
event, a collimated jet with obvious westward lateral motion firstly launched
from CBP1, during which a small bright point appeared around CBP1's east end,
and magnetic flux cancellation was observed within the eruption source region.
Based on these characteristics, we interpret the observed jet as a standard jet
associated with photosperic magnetic flux cancellation. About 15 minutes later,
the westward moving jet spire interacted with CBP2 and resulted in magnetic
reconnection between them, which caused the formation of the second jet above
CBP2 and the appearance of a bright loop system in-between the two CBPs. In
addition, we observed the writhing, kinking, and violent eruption of a small
kink structure close to CBP2's west end but inside the jet-base, which made the
second jet brighter and broader than the first one. These features suggest that
the second jet should be a blowout jet triggered by the magnetic reconnection
between CBP2 and the spire of the first jet. We conclude that the two
successive jets were physically connected to each other rather than a temporal
coincidence, and this observation also suggests that coronal jets can be
triggered by external eruptions or disturbances, besides internal magnetic
activities or magnetohydrodynamic instabilities.


Abstract:
  This paper compares notions of double sliceness for links. The main result is
to show that a large family of 2-component Montesinos links are not strongly
doubly slice despite being weakly doubly slice and having doubly slice
components. Our principal obstruction to strong double slicing comes by
considering branched double covers. To this end we prove a result classifying
Seifert fibered spaces which admit a smooth embeddings into integer homology
$S^1 \times S^3$s by maps inducing surjections on the first homology group. A
number of other results and examples pertaining to doubly slice links are also
given.


Abstract:
  To boost the capacity of the cellular system, the operators have started to
reuse the same licensed spectrum by deploying 4G LTE small cells (Femto Cells)
in the past. But in time, these small cell licensed spectrum is not sufficient
to satisfy future applications like augmented reality (AR)and virtual reality
(VR). Hence, cellular operators look for alternate unlicensed spectrum in Wi-Fi
5 GHz band, later 3GPP named as LTE Licensed Assisted Access (LAA). The recent
and current rollout of LAA deployments (in developed nations like the US)
provides an opportunity to understand coexistence profound ground truth. This
paper discusses a high-level overview of my past, present, and future research
works in the direction of small cell benefits. In the future, we shift the
focus onto the latest unlicensed band: 6 GHz, where the latest Wi-Fi version,
802.11ax, will coexist with the latest cellular technology, 5G New Radio(NR) in
unlicensed


Abstract:
  Controlling the activity of proteins with azobenzene photoswitches is a
potent tool for manipulating their biological function. With the help of light,
one can change e.g. binding affinities, control allostery or temper with
complex biological processes. Additionally, due to their intrinsically fast
photoisomerisation, azobenzene photoswitches can serve as triggers to initiate
out-of-equilibrium processes. Such switching of the activity, therefore,
initiates a cascade of conformational events, which can only be accessed with
time-resolved methods. In this Review, we will show how combining the potency
of azobenzene photoswitching with transient spectroscopic techniques helps to
disclose the order of events and provide an experimental observation of
biomolecular interactions in real-time. This will ultimately help us to
understand how proteins accommodate, adapt and readjust their structure to
answer an incoming signal and it will complete our knowledge of the dynamical
character of proteins.


Abstract:
  Nonuniform structure of low-density nuclear matter, known as nuclear pasta,
is expected to appear not only in the inner crust of neutron stars but also in
core-collapse supernova explosions and neutron-star mergers. We perform fully
three-dimensional calculations of inhomogeneous nuclear matter and neutron-star
matter in the low-density region using the Thomas-Fermi approximation. The
nuclear interaction is described in the relativistic mean-field approach with
the point-coupling interaction, where the meson exchange in each channel is
replaced by the contact interaction between nucleons. We investigate the
influence of nuclear symmetry energy and its density dependence on pasta
structures by introducing a coupling term between the isoscalar-vector and
isovector-vector interactions. It is found that the properties of pasta phases
in the neutron-rich matter are strongly dependent on the symmetry energy and
its slope. In addition to typical shapes like droplets, rods, slabs, tubes, and
bubbles, some intermediate pasta structures are also observed in cold stellar
matter with a relatively large proton fraction. We find that nonspherical
shapes are unlikely to be formed in neutron-star crusts, since the proton
fraction obtained in $\beta$ equilibrium is rather small. The inner crust
properties may lead to a visible difference in the neutron-star radius.


Abstract:
  This paper addresses the Mountain Pass Theorem for locally Lipschitz
functions on finite-dimensional vector spaces in terms of tangencies. Namely,
let $f \colon \mathbb R^n \to \mathbb R$ be a locally Lipschitz function with a
mountain pass geometry. Let $$c := \inf_{\gamma \in \mathcal
A}\max_{t\in[0,1]}f(\gamma(t)),$$ where $\mathcal{A}$ is the set of all
continuous paths joining $x^*$ to $y^*.$ We show that either $c$ is a critical
value of $f$ or $c$ is a tangency value at infinity of $f.$ This reduces to the
Mountain Pass Theorem of Ambrosetti and Rabinowitz in the case where the
function $f$ is definable (such as, semi-algebraic) in an o-minimal structure.


Abstract:
  Recent studies on metamorphic petrology as well as microstructural
observations suggest the influence of mechanical effects upon chemically active
metamorphic minerals. Thus, the understanding of such a coupling is crucial to
describe the dynamics of geomaterials. In this effort, we derive a
thermodynamically-consistent framework to characterize the evolution of
chemically active minerals. We model the metamorphic mineral assemblages as a
solid-species solution where the species mass transport and chemical reaction
drive the stress generation process. The theoretical foundations of the
framework rely on modern continuum mechanics, thermodynamics far from
equilibrium, and the phase-field model. We treat the mineral solid solution as
a continuum body, and following the Larch\'e and Cahn network model, we define
displacement and strain fields. Consequently, we obtain a set of coupled
chemo-mechanical equations. We use the aforementioned framework to study single
minerals as solid solutions during metamorphism. Furthermore, we emphasise the
use of the phase-field framework as a promising tool to model complex
multi-physics processes in geoscience. Without loss of generality, we use
common physical and chemical parameters found in the geoscience literature to
portrait a comprehensive view of the underlying physics. Thereby, we carry out
2D and 3D numerical simulations using material parameters for metamorphic
minerals to showcase and verify the chemo-mechanical interactions of mineral
solid solutions that undergo spinodal decomposition, chemical reactions, and
deformation.


Abstract:
  Reversible covalent kinase inhibitors (RCKIs) are a class of novel kinase
inhibitors attracting increasing attention because they simultaneously show the
selectivity of covalent kinase inhibitors, yet avoid permanent
protein-modification-induced adverse effects. Over the last decade, RCKIs have
been reported to target different kinases, including atypical kinases.
Currently, three RCKIs are undergoing clinical trials to treat specific
diseases, for example, Pemphigus, an autoimmune disorder. In this perspective,
first, RCKIs are systematically summarized, including characteristics of
electrophilic groups, chemical scaffolds, nucleophilic residues, and binding
modes. Second, we provide insights into privileged electrophiles, the
distribution of nucleophiles and hence effective design strategies for RCKIs.
Finally, we provide a brief perspective on future design strategies for RCKIs,
including those that target proteins other than kinases.


Abstract:
  We prove that the sublinearly Morse boundary of every known cubulated group
continuously injects in the Gromov boundary of a certain hyperbolic graph. We
also show that for all CAT(0) cube complexes, convergence to sublinearly Morse
geodesic rays has a simple combinatorial description using the hyperplanes
crossed by such sequences. As an application of this combinatorial description,
we show that a certain subspace of the Roller boundary continously surjects on
the subspace of the visual boundary consisting of sublinearly Morse geodesic
rays.


Abstract:
  Depth completion aims to generate a dense depth map from the sparse depth map
and aligned RGB image. However, current depth completion methods use extremely
expensive 64-line LiDAR(about $100,000) to obtain sparse depth maps, which will
limit their application scenarios. Compared with the 64-line LiDAR, the
single-line LiDAR is much less expensive and much more robust. Therefore, we
propose a method to tackle the problem of single-line depth completion, in
which we aim to generate a dense depth map from the single-line LiDAR info and
the aligned RGB image. A single-line depth completion dataset is proposed based
on the existing 64-line depth completion dataset(KITTI). A network called
Semantic Guided Two-Branch Network(SGTBN) which contains global and local
branches to extract and fuse global and local info is proposed for this task. A
Semantic guided depth upsampling module is used in our network to make full use
of the semantic info in RGB images. Except for the usual MSE loss, we add the
virtual normal loss to increase the constraint of high-order 3D geometry in our
network. Our network outperforms the state-of-the-art in the single-line depth
completion task. Besides, compared with the monocular depth estimation, our
method also has significant advantages in precision and model size.


Abstract:
  Spectral observations below Lyman-alpha are now obtained with the Cosmic
Origin Spectrograph (COS) on the Hubble Space Telescope (HST). It is therefore
necessary to provide an accurate treatment of the blue wing of the Lyman-alpha
line that enables correct calculations of radiative transport in DA and DBA
white dwarf stars. On the theoretical front, we very recently developed very
accurate H-He potential energies for the hydrogen 1s, 2s, and 2p states.
Nevertheless, an uncertainty remained about the asymptotic correlation of the
Sigma states and the electronic dipole transition moments. A similar difficulty
occurred in our first calculations for the resonance broadening of hydrogen
perturbed by collisions with neutral H atoms. The aim of this paper is twofold.
First, we clarify the question of the asymptotic correlation of the Sigma
states, and we show that relativistic contributions, even very tiny, may need
to be accounted for a correct long-range and asymptotic description of the
states because of the specific 2s 2p Coulomb degeneracy in hydrogen. This
effect of relativistic corrections, inducing small splitting of the 2s and 2p
states of H, is shown to be important for the Sigma-Sigma$ transition dipole
moments in H-He and is also discussed in H-H. Second, we use existent (H-H) and
newly determined (H-He) accurate potentials and properties to provide a
theoretical investigation of the collisional effects on the blue wing of the
Lyman-alpha line of H perturbed by He and H. We study the relative
contributions in the blue wing of the H and He atoms according to their
relative densities. We finally achieve a comparison with recent COS
observations and propose an assignment for a feature centered at 1190 A.


Abstract:
  We apply a recently developed unsupervised machine learning scheme for local
atomic environments to characterize large-scale, disordered aggregates formed
by sequence-defined macromolecules. This method provides new insight into the
structure of these disordered, dilute aggregates, which has proven difficult to
understand using collective variables manually derived from expert knowledge.
In contrast to such conventional order parameters, we are able to classify the
global aggregate structure directly using descriptions of the local
environments. The resulting characterization provides a deeper understanding of
the range of possible self-assembled structures and their relationships to each
other. We also provide a detailed analysis of the effects of finite system
size, stochasticity, and kinetics of these aggregates based on the learned
collective variables. Interestingly, we find that the spatiotemporal evolution
of systems in the learned latent space is smooth and continuous, despite being
derived from only a single snapshot from each of about 1000 monomer sequences.
These results demonstrate the insight which can be gained by applying
unsupervised machine learning to soft matter systems, especially when suitable
order parameters are not known.


Abstract:
  Recently, significant progress has been made in single-view depth estimation
thanks to increasingly large and diverse depth datasets. However, these
datasets are largely limited to specific application domains (e.g. indoor,
autonomous driving) or static in-the-wild scenes due to hardware constraints or
technical limitations of 3D reconstruction. In this paper, we introduce the
first depth dataset DynOcc consisting of dynamic in-the-wild scenes. Our
approach leverages the occlusion cues in these dynamic scenes to infer depth
relationships between points of selected video frames. To achieve accurate
occlusion detection and depth order estimation, we employ a novel occlusion
boundary detection, filtering and thinning scheme followed by a robust
foreground/background classification method. In total our DynOcc dataset
contains 22M depth pairs out of 91K frames from a diverse set of videos. Using
our dataset we achieved state-of-the-art results measured in weighted human
disagreement rate (WHDR). We also show that the inferred depth maps trained
with DynOcc can preserve sharper depth boundaries.


Abstract:
  Large global companies need to speed up their innovation activities to
increase competitive advantage. However, such companies' organizational
structures impede their ability to capture trends they are well aware of due to
bureaucracy, slow decision-making, distributed departments, and distributed
processes. One way to strengthen the innovation capability is through fostering
internal startups. We report findings from an embedded multiple-case study of
five internal startups in a globally distributed company to identify barriers
for software product innovation: late involvement of software developers,
executive sponsor is missing or not clarified, yearly budgeting and planning,
unclear decision-making authority, lack of digital infrastructure for
experimentation and access to data from external actors. Drawing on the
framework of continuous software engineering proposed by Fitzgerald and Stol,
we discuss the role of BizDev in software product innovation. We suggest that
lack of continuity, rather than the lack of speed, is an ultimate challenge for
internal startups in large global companies.


Abstract:
  We study the background (equilibrium), linear and nonlinear spin currents in
2D Rashba spin-orbit coupled systems with Zeeman splitting and in 3D
noncentrosymmetric metals using modified spin current operator by inclusion of
the anomalous velocity. The linear spin Hall current arises due to the
anomalous velocity of charge carriers induced by the Berry curvature. The
nonlinear spin current occurs due to the band velocity and/or the anomalous
velocity. For 2D Rashba systems, the background spin current saturates at high
Fermi energy (independent of the Zeeman coupling), linear spin current exhibits
a plateau at the Zeeman gap and nonlinear spin currents are peaked at the gap
edges. The magnitude of the nonlinear spin current peaks enhances with the
strength of Zeeman interaction. The linear spin current is polarized out of
plane, while the nonlinear ones are polarized in-plane. We witness pure
anomalous nonlinear spin current with spin polarization along the direction of
propagation. In 3D noncentrosymmetric metals, background and linear spin
currents are monotonically increasing functions of Fermi energy, while
nonlinear spin currents vary non-monotonically as a function of Fermi energy
and are independent of the Berry curvature. These findings may provide useful
information to manipulate spin currents in Rashba spin-orbit coupled systems.


Abstract:
  Using a navigation process with the datum $(F,V)$, in which $F$ is a Finsler
metric and the smooth tangent vector field $V$ satisfies $F(-V(x))>1$
everywhere, a Lorentz Finsler metric $\tilde{F}$ can be induced. Isoparametric
functions and isoparametric hypersurfaces with or without involving a smooth
measure can be defined for $\tilde{F}$. When the vector field $V$ in the
navigation datum is homothetic, we prove the local correspondences between
isoparametric functions and isoparametric hypersurfaces before and after this
navigation process. Using these correspondences, we provide some examples of
isoparametric functions and isoparametric hypersurfaces on a Funk space of
Lorentz Randers type.


Abstract:
  We study the high frequency Hall conductivity in a two-dimensional (2D) model
of conduction electrons coupled to a background magnetic skyrmion texture via
an effective Hund's coupling term. For an ordered skyrmion crystal, a Kubo
formula calculation using the basis of skyrmion crystal Chern bands reveals a
resonant Hall response at a frequency set by the Hund's coupling:
$\hbar\omega_{\text{res}} \approx J_H$. A complementary real-space Kubo formula
calculation for an isolated skyrmion in a box reveals a similar resonant Hall
response. A linear relation between the area under the Hall resonant curve and
the skyrmion density is discovered numerically and is further elucidated using
a gradient expansion which is valid for smooth textures and a local
approximation based on a spin-trimer calculation. We point out the issue of
distinguishing this skyrmion contribution from a similar feature arising from
spin-orbit interactions, as demonstrated in a model for Rashba spin-orbit
coupled electrons in a collinear ferromagnet, which is analogous to the
difficulty of unambiguously separating the d.c. topological Hall effect from
the anomalous Hall effect. The resonant feature in the high frequency
topological Hall effect is proposed to provide a potentially useful local
optical signature of skyrmions via probes such as scanning magneto-optical Kerr
microscopy.


Abstract:
  We report on observations of the active K2 dwarf $\epsilon$ Eridani based on
contemporaneous SPIRou, NARVAL, and TESS data obtained over two months in late
2018, when the activity of the star was reported to be in a non-cyclic phase.
We first recover the fundamental parameters of the target from both visible and
nIR spectral fitting. The large-scale magnetic field is investigated from
polarimetric data. From unpolarized spectra, we estimate the total magnetic
flux through Zeeman broadening of magnetically sensitive nIR lines and the
chromospheric emission using the CaII H & K lines. The TESS photometric
monitoring is modeled with pseudo-periodic Gaussian Process Regression.
Fundamental parameters of $\epsilon$ Eridani derived from visible and
near-infrared wavelengths provide us with consistent results, also in agreement
with published values. We report a progressive increase of macroturbulence
towards larger nIR wavelengths. Zeeman broadening of individual lines
highlights an unsigned surface magnetic field $B_{\rm mono} = 1.90 \pm 0.13$
kG, with a filling factor $f = 12.5 \pm 1.7$% (unsigned magnetic flux $Bf = 237
\pm 36$ G). The large-scale magnetic field geometry, chromospheric emission,
and broadband photometry display clear signs of non-rotational evolution over
the course of data collection. Characteristic decay times deduced from the
light curve and longitudinal field measurements fall in the range 30-40 d,
while the characteristic timescale of surface differential rotation, as derived
through the evolution of the magnetic geometry, is equal to $57 \pm 5$ d. The
large-scale magnetic field exhibits a combination of properties not observed
previously for $\epsilon$ Eridani, with a surface field among the weakest
previously reported, but also mostly axisymmetric, and dominated by a toroidal
component.


Abstract:
  Maintenance of existing software requires a large amount of time for
comprehending the source code. The architecture of a software, however, may not
be clear to maintainers if up to date documentations are not available.
Software clustering is often used as a remodularisation and architecture
recovery technique to help recover a semantic representation of the software
design. Due to the diverse domains, structure, and behaviour of software
systems, the suitability of different clustering algorithms for different
software systems are not investigated thoroughly. Research that introduce new
clustering techniques usually validate their approaches on a specific domain,
which might limit its generalisability. If the chosen test subjects could only
represent a narrow perspective of the whole picture, researchers might risk not
being able to address the external validity of their findings. This work aims
to fill this gap by introducing a new approach, Explaining Software Clustering
for Remodularisation, to evaluate the effectiveness of different software
clustering approaches. This work focuses on hierarchical clustering and Bunch
clustering algorithms and provides information about their suitability
according to the features of the software, which as a consequence, enables the
selection of the most optimum algorithm and configuration from our existing
pool of choices for a particular software system. The proposed framework is
tested on 30 open source software systems with varying sizes and domains, and
demonstrates that it can characterise both the strengths and weaknesses of the
analysed software clustering algorithms using software features extracted from
the code. The proposed approach also provides a better understanding of the
algorithms behaviour through the application of dimensionality reduction
techniques.


Abstract:
  Cleaner analytic technique for quantifying compounds in dense suspension is
needed for wastewater and environment analysis, chemical or bio-conversion
process monitoring, biomedical diagnostics, food quality control among others.
In this work, we introduce a green, fast, one-step method called nanoextraction
for extraction and detection of target analytes from sub-milliliter dense
suspensions using surface nanodroplets without toxic solvents and pre-removal
of the solid contents. With nanoextraction, we achieve a limit of detection
(LOD) of 10^(-9) M for a fluorescent model analyte obtained from a particle
suspension sample. The LOD lower than that in water without particles 10^(-8)
M, potentially due to the interaction of particles and the analyte. The high
particle concentration in the suspension sample thus does not reduce the
extraction efficiency, although the extraction process was slowed down up to 5
min. As proof of principle, we demonstrate the nanoextraction for
quantification of model compounds in wastewater slurry containing 30 wt% sands
and oily components (i.e. heavy oils). The nanoextraction and detection
technology developed in this work may be used as fast analytic technologies for
complex slurry samples in environment industrial waste, or in biomedical
diagnostics.


Abstract:
  We explicitly compute the homology groups with coefficients in a field of
characteristic zero of cocyclic subgroups or even Artin groups of FC-type. We
also give some partial results in the case when the coefficients are taken in a
field of prime characteristic.


Abstract:
  We prove that for all positive integers $n$ and $k$, there exists an integer
$N = N(n,k)$ satisfying the following. If $U$ is a set of $k$ direction vectors
in the plane and $\mathcal{J}_U$ is the set of all line segments in direction
$u$ for some $u\in U$, then for every $N$ families $\mathcal{F}_1, \ldots,
\mathcal{F}_N$, each consisting of $n$ mutually disjoint segments in
$\mathcal{J}_U$, there is a set $\{A_1, \ldots, A_n\}$ of $n$ disjoint segments
in $\bigcup_{1\leq i\leq N}\mathcal{F}_i$ and distinct integers $p_1, \ldots,
p_n\in \{1, \ldots, N\}$ satisfying that $A_j\in \mathcal{F}_{p_j}$ for all
$j\in \{1, \ldots, n\}$. We generalize this property for underlying lines on
fixed $k$ directions to $k$ families of simple curves with certain conditions.


Abstract:
  L. Moret-Bailly constructed families $\mathfrak{C}\rightarrow \mathbb{P}^1$
of genus 2 curves with supersingular jacobian. In this paper we first classify
the reducible fibers of a Moret-Bailly family using linear algebra over a
quaternion algebra. The main result is an algorithm that exploits properties of
two reducible fibers to compute a hyperelliptic model for any irreducible fiber
of a Moret-Bailly family.


Abstract:
  This paper presents a method for gaze estimation according to face images. We
train several gaze estimators adopting four different network architectures,
including an architecture designed for gaze estimation (i.e.,iTracker-MHSA) and
three originally designed for general computer vision tasks(i.e., BoTNet,
HRNet, ResNeSt). Then, we select the best six estimators and ensemble their
predictions through a linear combination. The method ranks the first on the
leader-board of ETH-XGaze Competition, achieving an average angular error of
$3.11^{\circ}$ on the ETH-XGaze test set.


Abstract:
  In a separable Hilbert space $X$, we study the controlled evolution equation
\begin{equation*} u'(t)+Au(t)+p(t)Bu(t)=0, \end{equation*} where $A\geq-\sigma
I$ ($\sigma\geq0$) is a self-adjoint linear operator, $B$ is a bounded linear
operator on $X$, and $p\in L^2_{loc}(0,+\infty)$ is a bilinear control.
  We give sufficient conditions in order for the above nonlinear control system
to be locally controllable to the $j$th eigensolution for any $j\geq1$. We also
derive semi-global controllability results in large time and discuss
applications to parabolic equations in low space dimension. Our method is
constructive and all the constants involved in the main results can be
explicitly computed.


Abstract:
  In the cuprates, one-dimensional chain compounds provide a unique opportunity
to understand the microscopic physics due to the availability of reliable
theories. However, progress has been limited by the inability to controllably
dope these materials. Here, we report the synthesis and spectroscopic analysis
of the one-dimensional cuprate Ba$_{2-x}$Sr$_x$CuO$_{3+\delta}$ over a wide
range of hole doping. Our angle-resolved photoemission experiments reveal the
doping evolution of the holon and spinon branches. We identify a prominent
folding branch whose intensity fails to match predictions of the simple Hubbard
model. An additional strong near-neighbor attraction, which may arise from
coupling to phonons, quantitatively explains experiments for all accessible
doping levels. Considering structural and quantum chemistry similarities among
cuprates, this attraction will play a similarly crucial role in the high-$T_C$
superconducting counterparts


Abstract:
  Antonio Colla was a meteorologist and astronomer who made sunspot
observations at the Meteorological Observatory of the Parma University (Italy).
He carried out his sunspot records from 1830 to 1843, just after the Dalton
Minimum. We have recovered 71 observation days for this observer.
Unfortunately, many of these records are qualitative and we could only obtain
the number of sunspot groups and/or single sunspots from 25 observations.
However, we highlight the importance of these records because Colla is not
included in the sunspot group database as an observer and, therefore, neither
his sunspot observations. According to the number of groups, the sunspot
observations made by Colla are similar as several observers of his time. For
common observation day, only Stark significantly recorded more groups than
Colla. Moreover, we have calculated the sunspot area and positions from Colla's
sunspot drawings concluding that both areas and positions recorded by this
observer seem unreal. Therefore, Colla's drawings can be interpreted such as
sketches including reliable information on the number of groups but the
information on sunspot areas and positions should not be used for scientific
purposes.


Abstract:
  All current approaches for statically enforcing differential privacy in
higher order languages make use of either linear or relational refinement
types. A barrier to adoption for these approaches is the lack of support for
expressing these "fancy types" in mainstream programming languages. For
example, no mainstream language supports relational refinement types, and
although Rust and modern versions of Haskell both employ some linear typing
techniques, they are inadequate for embedding enforcement of differential
privacy, which requires "full" linear types a la Girard. We propose a new type
system that enforces differential privacy, avoids the use of linear and
relational refinement types, and can be easily embedded in mainstream richly
typed programming languages such as Scala, OCaml and Haskell. We demonstrate
such an embedding in Haskell, demonstrate its expressiveness on case studies,
and prove that our type-based enforcement of differential privacy is sound.


Abstract:
  We develop a multicomponent lattice Boltzmann (LB) model for the 2D
Rayleigh--Taylor turbulence with a Shan-Chen pseudopotential implemented on
GPUs. In the immiscible case this method is able to accurately overcome the
inherent numerical complexity caused by the complicated structure of the
interface that appears in the fully developed turbulent regime. Accuracy of the
LB model is tested both for early and late stages of instability. For the
developed turbulent motion we analyze the balance between different terms
describing variations of the kinetic and potential energies. Then, we analyze
the role of interface in the energy balance, and also the effects of the
vorticity induced by the interface in the energy dissipation. Statistical
properties are compared for miscible and immiscible flows. Our results can also
be considered as a first validation step to extend the application of LB model
to 3D immiscible Rayleigh-Taylor turbulence.


Abstract:
  We present a geometrically exact nonlinear analysis of elastic in-plane beams
in the context of finite but small strain theory. The formulation utilizes the
full beam metric and obtains the complete analytic elastic constitutive model
by employing the exact relation between the reference and equidistant strains.
Thus, we account for the nonlinear strain distribution over the thickness of a
beam. In addition to the full analytical constitutive model, four simplified
ones are presented. Their comparison provides a thorough examination of the
influence of a beam's metric on the structural response. We show that the
appropriate formulation depends on the curviness of a beam at all
configurations. Furthermore, the nonlinear distribution of strain along the
thickness of strongly curved beams must be considered to obtain a complete and
accurate response.


Abstract:
  Autoencoders as tools behind anomaly searches at the LHC have the structural
problem that they only work in one direction, extracting jets with higher
complexity but not the other way around. To address this, we derive classifiers
from the latent space of (variational) autoencoders, specifically in Gaussian
mixture and Dirichlet latent spaces. In particular, the Dirichlet setup solves
the problem and improves both the performance and the interpretability of the
networks.


Abstract:
  We reconsider work of Elkalla on subnormal subgroups of 3-manifold groups,
giving essentially algebraic arguments that extend to the case of $PD_3$-groups
and group pairs. However the argument relies on an $L^2$-Betti number
hypothesis which has not yet been shown to hold in general.


Abstract:
  Crystals and other condensed matter systems described by density waves often
exhibit dislocations. Here we show, by considering the topology of the ground
state manifolds (GSMs) of such systems, that dislocations in the density phase
field always split into disclinations, and that the disclinations themselves
are constrained to sit at particular points in the GSM. Consequently, the
topology of the GSM forbids zero-energy dislocation glide, giving rise to a
Peirels-Nabarro barrier.


Abstract:
  In this paper, we present a model for semantic memory that allows machines to
collect information and experiences to become more proficient with time. Post
semantic analysis of the sensory and other related data, the processed
information is stored in the knowledge graph which is then used to comprehend
the work instructions expressed in natural language. This imparts industrial
robots cognitive behavior to execute the required tasks in a deterministic
manner. The paper outlines the architecture of the system along with an
implementation of the proposal.


Abstract:
  Magnetic Resonance Fingerprinting (MRF) is a method to extract quantitative
tissue properties such as T1 and T2 relaxation rates from arbitrary pulse
sequences using conventional magnetic resonance imaging hardware. MRF pulse
sequences have thousands of tunable parameters which can be chosen to maximize
precision and minimize scan time. Here we perform de novo automated design of
MRF pulse sequences by applying physics-inspired optimization heuristics. Our
experimental data suggests systematic errors dominate over random errors in MRF
scans under clinically-relevant conditions of high undersampling. Thus, in
contrast to prior optimization efforts, which focused on statistical error
models, we use a cost function based on explicit first-principles simulation of
systematic errors arising from Fourier undersampling and phase variation. The
resulting pulse sequences display features qualitatively different from
previously used MRF pulse sequences and achieve fourfold shorter scan time than
prior human-designed sequences of equivalent precision in T1 and T2.
Furthermore, the optimization algorithm has discovered the existence of MRF
pulse sequences with intrinsic robustness against shading artifacts due to
phase variation.


Abstract:
  The 5G mobile network brings several new features that can be applied to
existing and new applications. High reliability, low latency, and high data
rate are some of the features which fulfill the requirements of vehicular
networks. Vehicular networks aim to provide safety for road users and several
additional advantages such as enhanced traffic efficiency and in-vehicle
infotainment services. This paper summarizes the most important aspects of
NR-V2X, which is standardized by 3GPP, focusing on sidelink communication. The
main part of this work belongs to the 3GPP Rel-16, which is the first 3GPP
release for NR-V2X, and the work/study items of the future Rel-17


Abstract:
  Existing emotion-aware conversational models usually focus on controlling the
response contents to align with a specific emotion class, whereas empathy is
the ability to understand and concern the feelings and experience of others.
Hence, it is critical to learn the causes that evoke the users' emotion for
empathetic responding, a.k.a. emotion causes. To gather emotion causes in
online environments, we leverage counseling strategies and develop an
empathetic chatbot to utilize the causal emotion information. On a real-world
online dataset, we verify the effectiveness of the proposed approach by
comparing our chatbot with several SOTA methods using automatic metrics,
expert-based human judgements as well as user-based online evaluation.


Abstract:
  We investigate an M/M/1 queue operating in two switching environments, where
the switch is governed by a two-state time-homogeneous Markov chain. This model
allows to describe a system that is subject to regular operating phases
alternating with anomalous working phases or random repairing periods. We first
obtain the steady-state distribution of the process in terms of a generalized
mixture of two geometric distributions. In the special case when only one kind
of switch is allowed, we analyze the transient distribution, and investigate
the busy period problem. The analysis is also performed by means of a suitable
heavy-traffic approximation which leads to a continuous random process. Its
distribution satisfies a partial differential equation with randomly
alternating infinitesimal moments. For the approximating process we determine
the steady-state distribution, the transient distribution and a
first-passage-time density.


Abstract:
  Signomials are obtained by generalizing polynomials to allow for arbitrary
real exponents. This generalization offers great expressive power, but has
historically sacrificed the organizing principle of ``degree'' that is central
to polynomial optimization theory. We reclaim that principle here through the
concept of signomial rings, which we use to derive complete convex relaxation
hierarchies of upper and lower bounds for signomial optimization via sums of
arithmetic-geometric exponentials (SAGE) nonnegativity certificates. The
Positivstellensatz underlying the lower bounds relies on the concept of
conditional SAGE and removes regularity conditions required by earlier works,
such as convexity and Archimedeanity of the feasible set. Through worked
examples we illustrate the practicality of this hierarchy in areas such as
chemical reaction network theory and chemical engineering. These examples
include comparisons to direct global solvers (e.g., BARON and ANTIGONE) and the
Lasserre hierarchy (where appropriate). The completeness of our hierarchy of
upper bounds follows from a generic construction whereby a Positivstellensatz
for signomial nonnegativity over a compact set provides for arbitrarily strong
outer approximations of the corresponding cone of nonnegative signomials. While
working toward that result, we prove basic facts on the existence and
uniqueness of solutions to signomial moment problems.


Abstract:
  For a random walk in a uniformly elliptic and i.i.d. environment on $\mathbb
Z^d$ with $d \geq 4$, we show that the quenched and annealed large deviations
rate functions agree on any compact set contained in the boundary $\partial
\mathbb{D}:=\{ x \in \mathbb R^d : |x|_1 =1\}$ of their domain which does not
intersect any of the $(d-2)$-dimensional facets of $\partial \mathbb{D}$,
provided that the disorder of the environment is~low~enough. As a consequence,
we obtain a simple explicit formula for both rate functions on $\partial
\mathbb{D}$ at low disorder. In contrast to previous works, our results do not
assume any ballistic behavior of the random walk and are not restricted to
neighborhoods of any given point (on the boundary $\partial \mathbb{D}$). In
addition, our~results complement those in [BMRS19], where, using different
methods, we investigate the equality of the rate functions in the interior of
their domain. Finally, for a general parametrized family of environments,
we~show that the strength of disorder determines a phase transition in the
equality of both rate functions, in the sense that for each $x \in \partial
\mathbb{D}$ there exists $\varepsilon_x$ such that the two rate functions agree
at $x$ when the disorder is smaller than $\varepsilon_x$ and disagree when its
larger. This further reconfirms the idea, introduced in [BMRS19], that the
disorder of the environment is in general intimately related with the equality
of the rate functions.


Abstract:
  In this paper, we consider graphon particle systems with heterogeneous
mean-field type interactions and the associated finite particle approximations.
Under suitable growth (resp. convexity) assumptions, we obtain uniform-in-time
concentration estimates, over finite (resp. infinite) time horizon, for the
Wasserstein distance between the empirical measure and its limit, extending the
work of Bolley--Guillin--Villani.


Abstract:
  It is well known that entanglement can benefit quantum information processing
tasks. Quantum illumination, when first proposed, is surprising as
entanglement's benefit survives entanglement-breaking noise. Since then, many
efforts have been devoted to study quantum sensing in noisy scenarios. The
applicability of such schemes, however, is limited to a binary quantum
hypothesis testing scenario. In terms of target detection, such schemes
interrogate a single polarization-azimuth-elevation-range-Doppler resolution
bin at a time, limiting the impact to radar detection. We resolve this
binary-hypothesis limitation by proposing a quantum ranging protocol enhanced
by entanglement. By formulating a ranging task as a multiary hypothesis testing
problem, we show that entanglement enables a 6-dB advantage in the error
exponent against the optimal classical scheme. Moreover, the proposed ranging
protocol can also be utilized to implement a pulse-position modulated
entanglement-assisted communication protocol. Our ranging protocol reveals
entanglement's potential in general quantum hypothesis testing tasks and paves
the way towards a quantum-ranging radar with a provable quantum advantage.


Abstract:
  In this paper, channel estimation techniques and phase shift design for
intelligent reflecting surface (IRS)-empowered single-user multiple-input
multiple-output (SU-MIMO) systems are proposed. Among four channel estimation
techniques developed in the paper, the two novel ones, single-path approximated
channel (SPAC) and selective emphasis on rank-one matrices (SEROM), have low
training overhead to enable practical IRS-empowered SU-MIMO systems. SPAC is
mainly based on parameter estimation by approximating IRS-related channels as
dominant single-path channels. SEROM exploits IRS phase shifts as well as
training signals for channel estimation and easily adjusts its training
overhead. A closed-form solution for IRS phase shift design is also developed
to maximize spectral efficiency where the solution only requires basic linear
operations. Numerical results show that SPAC and SEROM combined with the
proposed IRS phase shift design achieve high spectral efficiency even with low
training overhead compared to existing methods.


Abstract:
  Our Galaxy and the nearby Andromeda galaxy (M31) are the most massive members
of the Local Group, and they seem to be a bound pair, despite the uncertainties
on the relative motion of the two galaxies. A number of studies have shown that
the two galaxies will likely undergo a close approach in the next 4$-$5 Gyr. We
used direct $N$-body simulations to model this interaction to shed light on the
future of the Milky Way - Andromeda system and for the first time explore the
fate of the two supermassive black holes (SMBHs) that are located at their
centers. We investigated how the uncertainties on the relative motion of the
two galaxies, linked with the initial velocities and the density of the diffuse
environment in which they move, affect the estimate of the time they need to
merge and form ``Milkomeda''. After the galaxy merger, we follow the evolution
of their two SMBHs up to their close pairing and fusion. Upon the fiducial set
of parameters, we find that Milky Way and Andromeda will have their closest
approach in the next 4.3 Gyr and merge over a span of 10 Gyr. Although the time
of the first encounter is consistent with other predictions, we find that the
merger occurs later than previously estimated. We also show that the two SMBHs
will spiral in the inner region of Milkomeda and coalesce in less than 16.6 Myr
after the merger of the two galaxies. Finally, we evaluate the
gravitational-wave emission caused by the inspiral of the SMBHs, and we discuss
the detectability of similar SMBH mergers in the nearby Universe ($z\leq 2$)
through next-generation gravitational-wave detectors.


Abstract:
  With the emerging needs of creating fairness-aware solutions for search and
recommendation systems, a daunting challenge exists of evaluating such
solutions. While many of the traditional information retrieval (IR) metrics can
capture the relevance, diversity and novelty for the utility with respect to
users, they are not suitable for inferring whether the presented results are
fair from the perspective of responsible information exposure. On the other
hand, various fairness metrics have been proposed but they do not account for
the user utility or do not measure it adequately. To address this problem, we
propose a new metric called Fairness-Aware IR (FAIR). By unifying standard IR
metrics and fairness measures into an integrated metric, this metric offers a
new perspective for evaluating fairness-aware ranking results. Based on this
metric, we developed an effective ranking algorithm that jointly optimized user
utility and fairness. The experimental results showed that our FAIR metric
could highlight results with good user utility and fair information exposure.
We showed how FAIR related to existing metrics and demonstrated the
effectiveness of our FAIR-based algorithm. We believe our work opens up a new
direction of pursuing a computationally feasible metric for evaluating and
implementing the fairness-aware IR systems.


Abstract:
  Real-Time Networks (RTNs) provide latency guarantees for time-critical
applications and it aims to support different traffic categories via various
scheduling mechanisms. Those scheduling mechanisms rely on a precise network
performance measurement to dynamically adjust the scheduling strategies.
Machine Learning (ML) offers an iterative procedure to measure network
performance. Network Calculus (NC) can calculate the bounds for the main
performance indexes such as latencies and throughputs in an RTN for ML. Thus,
the ML and NC integration improve overall calculation efficiency. This paper
will provide a survey for different approaches of Real-Time Network performance
measurement via NC as well as ML and present their results, dependencies, and
application scenarios.


Abstract:
  Given a bipartite graph with bipartition $(A,B)$ where $B$ is equipartitioned
into $k\ge2$ blocks, can the vertices in $A$ be picked one by one so that at
every step, the picked vertices cover roughly the same number of vertices in
each of these blocks? We show that, if each block has cardinality $m$, the
vertices in $B$ have the same degree, and each vertex in $A$ has at most $cm$
neighbors in every block where $c>0$ is a small constant, then there is an
ordering $v_1,\ldots,v_n$ of the vertices in $A$ such that for every
$j\in\{1,\ldots,n\}$, the numbers of vertices with a neighbor in
$\{v_1,\ldots,v_j\}$ in every two blocks differ by at most $\sqrt{2(k-1)c}\cdot
m$. This is related to a well-known lemma of Steinitz, and partially answers an
unpublished question of Scott and Seymour.


Abstract:
  Software verification may yield spurious failures when environment
assumptions are not accounted for. Environment assumptions are the expectations
that a system or a component makes about its operational environment and are
often specified in terms of conditions over the inputs of that system or
component. In this article, we propose an approach to automatically infer
environment assumptions for Cyber-Physical Systems (CPS). Our approach improves
the state-of-the-art in three different ways: First, we learn assumptions for
complex CPS models involving signal and numeric variables; second, the learned
assumptions include arithmetic expressions defined over multiple variables;
third, we identify the trade-off between soundness and informativeness of
environment assumptions and demonstrate the flexibility of our approach in
prioritizing either of these criteria.
  We evaluate our approach using a public domain benchmark of CPS models from
Lockheed Martin and a component of a satellite control system from LuxSpace, a
satellite system provider. The results show that our approach outperforms
state-of-the-art techniques on learning assumptions for CPS models, and
further, when applied to our industrial CPS model, our approach is able to
learn assumptions that are sufficiently close to the assumptions manually
developed by engineers to be of practical value.


Abstract:
  A fully discrete and fully explicit low-regularity integrator is constructed
for the one-dimensional periodic cubic nonlinear Schr\"odinger equation. The
method can be implemented by using fast Fourier transform with $O(N\ln N)$
operations at every time level, and is proved to have an $L^2$-norm error bound
of $O(\tau\sqrt{\ln(1/\tau)}+N^{-1})$ for $H^1$ initial data, without requiring
any CFL condition, where $\tau$ and $N$ denote the temporal stepsize and the
degree of freedoms in the spatial discretisation, respectively.


Abstract:
  Isotropic hyper-elasticity, altogether with the equilibrium equation and the
usual boundary conditions, are formulated directly on the body B, a
three-dimensional compact and orientable manifold with boundary equipped with a
mass measure. Pearson-Sewell-Beatty pressure potential is formulated in an
intrinsic geometric manner. It is shown that Poincar{\'e}'s formula extended to
infinite dimension, provides, in a straightforward manner, the optimal
(non-holonomic) constraints for such a pressure potential to exist.


Abstract:
  Rare-earth titanates are Mott insulators whose magnetic ground state --
antiferromagnetic (AFM) or ferromagnetic (FM) -- can be tuned by the radius of
the rare-earth element. Here, we combine phenomenology and first-principles
calculations to shed light on the generic magnetic phase diagram of a
chemically-substituted titanate on the rare-earth site that interpolates
between an AFM and a FM state. Octahedral rotations present in these
perovskites cause the AFM order to acquire a small FM component -- and
vice-versa -- removing any multi-critical point from the phase diagram.
However, for a wide parameter range, a first-order metamagnetic transition line
terminating at a critical end-point survives inside the magnetically ordered
phase. Similarly to the liquid-gas transition, a Widom line emerges from the
end-point, characterized by enhanced fluctuations. In contrast to metallic
ferromagnets, this metamagnetic transition involves two symmetry-equivalent and
insulating canted spin states. Moreover, instead of a magnetic field, we show
that uniaxial strain can be used to tune this transition to zero-temperature,
inducing a quantum critical end-point.


Abstract:
  Machine learning models $-$ now commonly developed to screen, diagnose, or
predict health conditions $-$ are evaluated with a variety of performance
metrics. An important first step in assessing the practical utility of a model
is to evaluate its average performance over an entire population of interest.
In many settings, it is also critical that the model makes good predictions
within predefined subpopulations. For instance, showing that a model is fair or
equitable requires evaluating the model's performance in different demographic
subgroups. However, subpopulation performance metrics are typically computed
using only data from that subgroup, resulting in higher variance estimates for
smaller groups. We devise a procedure to measure subpopulation performance that
can be more sample-efficient than the typical subsample estimates. We propose
using an evaluation model $-$ a model that describes the conditional
distribution of the predictive model score $-$ to form model-based metric (MBM)
estimates. Our procedure incorporates model checking and validation, and we
propose a computationally efficient approximation of the traditional
nonparametric bootstrap to form confidence intervals. We evaluate MBMs on two
main tasks: a semi-synthetic setting where ground truth metrics are available
and a real-world hospital readmission prediction task. We find that MBMs
consistently produce more accurate and lower variance estimates of model
performance for small subpopulations.