Abstract:
  In this work, we present an alternative methodology to solve the
particle-fluid interaction in the resolved CFDEM coupling framework. This
numerical approach consists of coupling a Discrete Element Method (DEM) with a
Computational Fluid Dynamics (CFD) scheme, solving the motion of immersed
particles in a fluid phase. As a novelty, our approach explicitly accounts for
the body force acting on the fluid phase when computing the local momentum
balance equations. Accordingly, we implement a fluid-particle interaction
computing the buoyant and drag forces as a function of local shear strain and
pressure gradient. As a benchmark, we study the Stokesian limit of a single
particle. The validation is performed comparing our outcomes with the ones
provided by a previous resolved methodology and the analytical prediction. In
general, we find that the new implementation reproduces with very good accuracy
the Stokesian dynamics. Complementarily, we study the settling terminal
velocity of a sphere under confined conditions.


Abstract:
  Semantic segmentation is one of the basic, yet essential scene understanding
tasks for an autonomous agent. The recent developments in supervised machine
learning and neural networks have enjoyed great success in enhancing the
performance of the state-of-the-art techniques for this task. However, their
superior performance is highly reliant on the availability of a large-scale
annotated dataset. In this paper, we propose a novel fully unsupervised
semantic segmentation method, the so-called Information Maximization and
Adversarial Regularization Segmentation (InMARS). Inspired by human perception
which parses a scene into perceptual groups, rather than analyzing each pixel
individually, our proposed approach first partitions an input image into
meaningful regions (also known as superpixels). Next, it utilizes
Mutual-Information-Maximization followed by an adversarial training strategy to
cluster these regions into semantically meaningful classes. To customize an
adversarial training scheme for the problem, we incorporate adversarial pixel
noise along with spatial perturbations to impose photometrical and geometrical
invariance on the deep neural network. Our experiments demonstrate that our
method achieves the state-of-the-art performance on two commonly used
unsupervised semantic segmentation datasets, COCO-Stuff, and Potsdam.


Abstract:
  Continuing [Fuchino, Ottenbreit and Sakai[9, 10]] and [Fuchino and
Ottenbreit[11]], we further study reflection principles in connection with the
L\"owenheim-Skolem Theorems of stationary logics. In this paper, we mainly
analyze the situations in the models obtained by mixed support iteration of a
supercompact length and then collapsing another supercompact cardinal to make
it $(2^{\aleph_0})^+$. We show, among other things, that the reflection down to
$< 2^{\aleph_0}$ of the non-metrizability of topological spaces with small
character is independent from the reflection properties studied in [Fuchino,
Ottenbreit and Sakai[9, 10]] and [Fuchino and Ottenbreit[11]].


Abstract:
  We review the dispersion-theoretical analysis of the electromagnetic form
factors of the nucleon. We emphasize in particular the role of unitarity and
analyticity in the construction of the isoscalar and isovector spectral
functions. We present new results on the extraction of the nucleon radii, the
electric and magnetic form factors and the extraction of $\omega$-meson
couplings. All this is supplemented by a detailed calculation of the
theoretical uncertainties, using bootstrap and Bayesian methods to pin down the
statistical errors, while systematic errors are determined from variations of
the spectral functions. We also discuss the physics of the time-like form
factors and point out further issues to be addressed in this framework.


Abstract:
  Near-infrared to visible (NIR-VIS) face recognition is the most common case
in heterogeneous face recognition, which aims to match a pair of face images
captured from two different modalities. Existing deep learning based methods
have made remarkable progress in NIR-VIS face recognition, while it encounters
certain newly-emerged difficulties during the pandemic of COVID-19, since
people are supposed to wear facial masks to cut off the spread of the virus. We
define this task as NIR-VIS masked face recognition, and find it problematic
with the masked face in the NIR probe image. First, the lack of masked face
data is a challenging issue for the network training. Second, most of the
facial parts (cheeks, mouth, nose etc.) are fully occluded by the mask, which
leads to a large amount of loss of information. Third, the domain gap still
exists in the remaining facial parts. In such scenario, the existing methods
suffer from significant performance degradation caused by the above issues. In
this paper, we aim to address the challenge of NIR-VIS masked face recognition
from the perspectives of training data and training method. Specifically, we
propose a novel heterogeneous training method to maximize the mutual
information shared by the face representation of two domains with the help of
semi-siamese networks. In addition, a 3D face reconstruction based approach is
employed to synthesize masked face from the existing NIR image. Resorting to
these practices, our solution provides the domain-invariant face representation
which is also robust to the mask occlusion. Extensive experiments on three
NIR-VIS face datasets demonstrate the effectiveness and
cross-dataset-generalization capacity of our method.


Abstract:
  In this paper, we mainly study the global well-posedness and $L^2$ decay rate
for the strong solutions of the compressible co-rotation finite extensible
nonlinear elastic (FENE) dumbbell model with small initial data. This model is
a coupling of isentropic compressible Navier-Stokes equations with a nonlinear
Fokker-Planck equation. We first prove that the FENE dumbbell model admits a
unique global strong solution provided the initial data are close to
equilibrium state for $d\geq 2$. Moreover, for $d\geq3$, we show that optimal
$L^2$ decay rates of global strong solutions by the linear spectral theory.


Abstract:
  The Terahertz band is envisioned to meet the demanding 100 Gbps data rates
for 6G wireless communications. Aiming at combating the distance limitation
problem with low hardware-cost, ultra-massive MIMO with hybrid beamforming is
promising. However, relationships among wavelength, array size and antenna
spacing give rise to the inaccuracy of planar-wave channel model (PWM), while
an enlarged channel matrix dimension leads to excessive parameters of applying
spherical-wave channel model (SWM). Moreover, due to the adoption of hybrid
beamforming, channel estimation (CE) needs to recover high-dimensional channels
from severely compressed channel observation. In this paper, a hybrid
spherical- and planar-wave channel model (HSPM) is investigated and proved to
be accurate and efficient by adopting PWM within subarray and SWM among
subarray. Furthermore, a two-phase HSPM CE mechanism is developed. A deep
convolutional-neural-network (DCNN) is designed in the first phase for
parameter estimation of reference subarrays, while geometric relationships of
the remaining channel parameters between reference subarrays are leveraged to
complete CE in the second phase. Extensive numerical results demonstrate the
HSPM is accurate at various communication distances, array sizes and carrier
frequencies.The DCNN converges fast and achieves high accuracy with 5.2 dB
improved normalized-mean-square-error compared to literature methods, and owns
substantially low complexity.


Abstract:
  Current two-stage TTS framework typically integrates an acoustic model with a
vocoder -- the acoustic model predicts a low resolution intermediate
representation such as Mel-spectrum while the vocoder generates waveform from
the intermediate representation. Although the intermediate representation is
served as a bridge, there still exists critical mismatch between the acoustic
model and the vocoder as they are commonly separately learned and work on
different distributions of representation, leading to inevitable artifacts in
the synthesized speech. In this work, different from using pre-designed
intermediate representation in most previous studies, we propose to use VAE
combining with GAN to learn a latent representation directly from speech and
then utilize a flow-based acoustic model to model the distribution of the
latent representation from text. In this way, the mismatch problem is migrated
as the two stages work on the same distribution. Results demonstrate that the
flow-based acoustic model can exactly model the distribution of our learned
speech representation and the proposed TTS framework, namely Glow-WaveGAN, can
produce high fidelity speech outperforming the state-of-the-art GAN-based
model.


Abstract:
  Lazy evaluation is a powerful tool for functional programmers. It enables the
concise expression of on-demand computation and a form of compositionality not
available under other evaluation strategies. However, the stateful nature of
lazy evaluation makes it hard to analyze a program's computational cost, either
informally or formally. In this work, we present a novel and simple framework
for formally reasoning about lazy computation costs based on a recent model of
lazy evaluation: clairvoyant call-by-value. The key feature of our framework is
its simplicity, as expressed by our definition of the clairvoyance monad. This
monad is both simple to define (around 20 lines of Coq) and simple to reason
about. We show that this monad can be effectively used to mechanically reason
about the computational cost of lazy functional programs written in Coq.


Abstract:
  Complex lattices provide a versatile ground for fascinating quantum many-body
physics. Here, we propose an exotic mechanics for generating orbital
frustration in hexagonal lattices. We study two-component (pseudospin-$1/2$)
Bose gases in $p$-orbital bands of two-dimensional hexagonal lattices, and find
that the system exhibits previously untouched orbital frustration as a result
of the interplay of spin and orbital degrees of freedom, in contrast to normal
Ising-type orbital ordering of spinless $p$-orbital band bosons in
two-dimensional hexagonal lattices. Based on the classification by symmetry
analysis, we find the interplay of orbital frustration and strong interaction
leads to exotic Mott and superfluid phases with spin-orbital intertwined
orders, in spite of the complete absence of spin-orbital interaction in the
Hamiltonian. Our study implies many-body correlations in a multi-orbital
setting could induce rich spin-orbital intertwined physics in complex lattice
structures.


Abstract:
  Instance segmentation can detect where the objects are in an image, but hard
to understand the relationship between them. We pay attention to a typical
relationship, relative saliency. A closely related task, salient object
detection, predicts a binary map highlighting a visually salient region while
hard to distinguish multiple objects. Directly combining two tasks by
post-processing also leads to poor performance. There is a lack of research on
relative saliency at present, limiting the practical applications such as
content-aware image cropping, video summary, and image labeling.
  In this paper, we study the Salient Object Ranking (SOR) task, which manages
to assign a ranking order of each detected object according to its visual
saliency. We propose the first end-to-end framework of the SOR task and solve
it in a multi-task learning fashion. The framework handles instance
segmentation and salient object ranking simultaneously. In this framework, the
SOR branch is independent and flexible to cooperate with different detection
methods, so that easy to use as a plugin. We also introduce a
Position-Preserved Attention (PPA) module tailored for the SOR branch. It
consists of the position embedding stage and feature interaction stage.
Considering the importance of position in saliency comparison, we preserve
absolute coordinates of objects in ROI pooling operation and then fuse
positional information with semantic features in the first stage. In the
feature interaction stage, we apply the attention mechanism to obtain
proposals' contextualized representations to predict their relative ranking
orders. Extensive experiments have been conducted on the ASR dataset. Without
bells and whistles, our proposed method outperforms the former state-of-the-art
method significantly. The code will be released publicly available.


Abstract:
  We investigate the Hubbard Hamiltonian on ladders where the number of sites
per rung alternates between two and three. These geometries are bipartite, with
a non-equal number of sites on the two sublattices. Thus they share a key
feature of the Hubbard model in a class of lattices which Lieb has shown
analytically to exhibit long-range ferrimagnetic order, while being amenable to
powerful numeric approaches developed for quasi-one-dimensional geometries. The
Density Matrix Renormalization Group (DMRG) method is used to obtain the ground
state properties, e.g. excitation gaps, charge and spin densities as well as
their correlation functions at half-filling. We show the existence of
long-range ferrimagnetic order in the one-dimensional ladder geometries. Our
work provides detailed quantitative results which complement the general
theorem of Lieb for generalized bipartite lattices. It also addresses the issue
of how the alternation between quasi-long range order and spin liquid behavior
for uniform ladders with odd and even numbers of legs might be affected by a
regular alternation pattern.


Abstract:
  The CMS collaboration reported a $\sim 3 \, \sigma$ (local) excess at
$96\;$GeV in the search for light Higgs-boson decaying into two photons. This
mass coincides with a $\sim 2 \, \sigma$ (local) excess in the $b\bar b$ final
state at LEP. We show an interpretation of these possible signals as the
lightest Higgs boson in the 2 Higgs Doublet Model with an additional complex
Higgs singlet (2HDMS). The interpretation is in agreement with all experimental
and theoretical constraints. We concentrate on the 2HDMS type II, which
resembles the Higgs and Yukawa structure of the Next-to Minimal Supersymmetric
Standard Model. We discuss the experimental prospects for constraining our
explanation at future $e^+e^-$ colliders, with concrete analyses based on the
ILC prospects.


Abstract:
  We use the Hyper Suprime-Cam Subaru Strategic Program S19A shape catalog to
construct weak lensing shear-selected cluster samples. From aperture mass maps
covering $\sim 510$~deg$^2$ created using a truncated Gaussian filter, we
construct a catalog of 187 shear-selected clusters that correspond to mass map
peaks with the signal-to-noise ratio larger than 4.7. Most of the
shear-selected clusters have counterparts in optically-selected clusters, from
which we estimate the purity of the catalog to be higher than 95\%. The sample
can be expanded to 418 shear-selected clusters with the same signal-to-noise
ratio cut by optimizing the shape of the filter function and by combining weak
lensing mass maps created with several different background galaxy selections.
We argue that dilution and obscuration effects of cluster member galaxies can
be mitigated by using background source galaxy samples and adopting the filter
function with its inner boundary larger than about $2'$. The large samples of
shear-selected clusters that are selected without relying on any baryonic
tracer are useful for detailed studies of cluster astrophysics and cosmology.


Abstract:
  Optical, reflected light eclipse observations provide a direct probe of the
exoplanet scattering properties, such as from aerosols. We present here the
photometric, reflected light observations of WASP-43b using the HST WFC3/UVIS
instrument with the F350LP filter (346-822nm) encompassing the entire optical
band. This is the first reflected light, photometric eclipse using UVIS in
scanning mode; as such we further detail our scanning extraction and analysis
pipeline Arctor. Our HST WFC3/UVIS eclipse light curve for WASP-43 b derived a
3-{\sigma} upper limit of 67 ppm on the eclipse depth, which implies that
WASP-43b has a very dark dayside atmosphere. With our atmospheric modeling
campaign, we compared our reflected light constraints with predictions from
global circulation and cloud models, benchmarked with HST and Spitzer
observations of WASP-43b. We infer that we do not detect clouds on the dayside
within the pressure levels probed by HST WFC3/UVIS with the F350LP filter (P >
1 bar). This is consistent with the GCM predictions based on previous WASP-43b
observations. Dayside emission spectroscopy results from WASP-43b with HST and
Spitzer observations are likely to not be significantly affected by
contributions from cloud particles.


Abstract:
  Scalar-tensor theories leaving significant modifications of gravity at
cosmological scales rely on screening mechanisms to recover General Relativity
(GR) in high-density regions and pass stringent tests with astrophysical
objects. Much focus has been placed on the signatures of such modifications of
gravity on the propagation of gravitational waves (GWs) through cosmological
distances while typically assuming their emission from fully screened regions
with the wave generation strictly abiding by GR. Here, we closely analyse the
impact of screening mechanisms on the inspiral GW waveforms from compact
sources by employing a scaling method that enables a post-Newtonian (PN)
expansion in screened regimes. Particularly, we derive the leading-order
corrections to a fully screened emission to first PN order in the near zone and
we also compute the modifications in the unscreened radiation zone to second PN
order. For a concrete example, we apply our results to a cubic Galileon model.
The resulting GW amplitude from a binary black hole inspiral deviate from its
GR counterpart at most by one part in $10^{2}$ for the modifications in the
radiation zone and at most one part in $10^{11}$ due to next-order corrections
to the fully screened near zone. We expect such modifications to be
undetectable by the current generation of GW detectors, but the deviation is
not so small as to remain undetectable in future experiments.


Abstract:
  Accurate segmentation of brain tumors from multi-modal Magnetic Resonance
(MR) images is essential in brain tumor diagnosis and treatment. However, due
to the existence of domain shifts among different modalities, the performance
of networks decreases dramatically when training on one modality and performing
on another, e.g., train on T1 image while performing on T2 image, which is
often required in clinical applications. This also prohibits a network from
being trained on labeled data and then transferred to unlabeled data from a
different domain. To overcome this, unsupervised domain adaptation (UDA)
methods provide effective solutions to alleviate the domain shift between
labeled source data and unlabeled target data. In this paper, we propose a
novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA
scheme. Specifically, a bidirectional image synthesis and segmentation module
is proposed to segment the brain tumor using the intermediate data
distributions generated for the two domains, which includes an image-to-image
translator and a shared-weighted segmentation network. Further, a
global-to-local consistency learning module is proposed to build robust
representation alignments in an integrated way. Extensive experiments on a
multi-modal brain MR benchmark dataset demonstrate that the proposed method
outperforms several state-of-the-art unsupervised domain adaptation methods by
a large margin, while a comprehensive ablation study validates the
effectiveness of each key component. The implementation code of our method will
be released at \url{https://github.com/KeleiHe/BiGL}.


Abstract:
  We report on controllable cavity modes through controlling the backscattering
by two identical scatterers. Periodic changes of the backscattering coupling
between two degenerate cavity modes are observed with the angle between two
scatterers and elucidated by a theoretical model using two-mode approximation
and numerical simulations. The periodically appearing single-peak cavity modes
indicate mode degeneracy at diabolical points. Then interactions between single
quantum dots and cavity modes are investigated. Enhanced emission of a quantum
dot with a six-fold intensity increase is obtained in a microdisk at a
diabolical point. This method to control cavity modes allows large-scale
integration, high reproducibility and fexible design of the size, location,
quantity and shape for scatterers, which can be applied for integrated photonic
structures with scatterer-modified light-matter interaction.


Abstract:
  Statistical fault localization (SFL) techniques use execution profiles and
success/failure information from software executions, in conjunction with
statistical inference, to automatically score program elements based on how
likely they are to be faulty. SFL techniques typically employ one type of
profile data: either coverage data, predicate outcomes, or variable values.
Most SFL techniques actually measure correlation, not causation, between
profile values and success/failure, and so they are subject to confounding bias
that distorts the scores they produce. This paper presents a new SFL technique,
named \emph{UniVal}, that uses causal inference techniques and machine learning
to integrate information about both predicate outcomes and variable values to
more accurately estimate the true failure-causing effect of program statements.
\emph{UniVal} was empirically compared to several coverage-based,
predicate-based, and value-based SFL techniques on 800 program versions with
real faults.


Abstract:
  We prove upper bounds on the graph diameters of polytopes in two settings.
The first is a worst-case bound for integer polytopes in terms of the length of
the description of the polytope (in bits) and the minimum angle between facets
of its polar. The second is a smoothed analysis bound: given an appropriately
normalized polytope, we add small Gaussian noise to each constraint. We
consider a natural geometric measure on the vertices of the perturbed polytope
(corresponding to the mean curvature measure of its polar) and show that with
high probability there exists a "giant component" of vertices, with measure
$1-o(1)$ and polynomial diameter. Both bounds rely on spectral gaps -- of a
certain Schr\"odinger operator in the first case, and a certain continuous time
Markov chain in the second -- which arise from the log-concavity of the volume
of a simple polytope in terms of its slack variables.


Abstract:
  A data-driven convergence criterion for the D'Agostini (Richardson-Lucy)
iterative unfolding is presented. It relies on the unregularized spectrum
(infinite number of iterations), and allows a safe estimation of the bias and
undercoverage induced by truncating the algorithm. In addition, situations
where the response matrix is not perfectly known are also discussed, and show
that in most cases the unregularized spectrum is not an unbiased estimator of
the true distribution. Whenever a bias is introduced, either by truncation of
by poor knowledge of the response, a way to retrieve appropriate coverage
properties is proposed.


Abstract:
  Warfarin, a commonly prescribed drug to prevent blood clots, has a highly
variable individual response. Determining a maintenance warfarin dose that
achieves a therapeutic blood clotting time, as measured by the international
normalized ratio (INR), is crucial in preventing complications. Machine
learning algorithms are increasingly being used for warfarin dosing; usually,
an initial dose is predicted with clinical and genotype factors, and this dose
is revised after a few days based on previous doses and current INR. Since a
sequence of prior doses and INR better capture the variability in individual
warfarin response, we hypothesized that longitudinal dose response data will
improve maintenance dose predictions. To test this hypothesis, we analyzed a
dataset from the COAG warfarin dosing study, which includes clinical data,
warfarin doses and INR measurements over the study period, and maintenance dose
when therapeutic INR was achieved. Various machine learning regression models
to predict maintenance warfarin dose were trained with clinical factors and
dosing history and INR data as features. Overall, dose revision algorithms with
a single dose and INR achieved comparable performance as the baseline dose
revision algorithm. In contrast, dose revision algorithms with longitudinal
dose and INR data provided maintenance dose predictions that were statistically
significantly much closer to the true maintenance dose. Focusing on the best
performing model, gradient boosting (GB), the proportion of ideal estimated
dose, i.e., defined as within $\pm$20% of the true dose, increased from the
baseline (54.92%) to the GB model with the single (63.11%) and longitudinal
(75.41%) INR. More accurate maintenance dose predictions with longitudinal dose
response data can potentially achieve therapeutic INR faster, reduce
drug-related complications and improve patient outcomes with warfarin therapy.


Abstract:
  The development of alternative platforms for computing has been a
longstanding goal for physics, and represents a particularly pressing concern
as conventional transistors approach the limit of miniaturization. A potential
alternatice paradigm is that of reservoir computing, which leverages unknown,
but highly non-linear transformations of input-data to perform computations.
This has the advantage that many physical systems exhibit precisely the type of
non-linear input-output relationships necessary for them to function as
reservoirs. Consequently, the quantum effects which obstruct the further
development of silicon electronics become an advantage for a reservoir
computer. Here we demonstrate that even the most basic constituents of matter -
atoms - can act as a reservoir for optical computers, thanks to the phenomenon
of High Harmonic Generation (HHG). A prototype single-atom computer for
classification problems is proposed, where parameters of the classification
model are mapped to optical elements. We numerically demonstrate that this
`all-optical' computer can successfully classify data with an accuracy that is
strongly dependent on dynamical non-linearities. This may pave the way for the
development of petahertz information processing platforms.


Abstract:
  Rapid antigen tests detect proteins at the surface of virus particles,
identifying the disease during its infectious phase. In contrast, PCR tests
detect viral genomes; they can thus diagnose COVID-19 before the infectious
phase but also react to remnants of the virus genome, even weeks after live
virus ceases to be detectable in the respiratory tract. Furthermore, the
logistics for administering the tests are different, with rapid antigen tests
being much easier to administer at-scale. In this article, we discuss the
relative advantages of the different testing procedures and summarise evidence
that shows that using antigen tests 2-3 times per week could become a powerful
tool to suppress the COVID-19 pandemic. We also discuss the results of recent
large-scale rapid antigen testing in Austrian schools. While our report on
testing predates Delta, we have updated the review with recent data on viral
loads in breakthrough infections and more information about testing efficacy,
especially in children.


Abstract:
  Collective oscillations and their suppression by external stimulation are
analyzed in a large-scale neural network consisting of two interacting
populations of excitatory and inhibitory quadratic integrate-and-fire neurons.
In the limit of an infinite number of neurons, the microscopic model of this
network can be reduced to an exact low-dimensional system of mean-field
equations. Bifurcation analysis of these equations reveals three different
dynamic modes in a free network: a stable resting state, a stable limit cycle,
and bistability with a coexisting resting state and a limit cycle. We show that
in the limit cycle mode, high-frequency stimulation of an inhibitory population
can stabilize an unstable resting state and effectively suppress collective
oscillations. We also show that in the bistable mode, the dynamics of the
network can be switched from a stable limit cycle to a stable resting state by
applying an inhibitory pulse to the excitatory population. The results obtained
from the mean-field equations are confirmed by numerical simulation of the
microscopic model.


Abstract:
  Site Reliability Engineers (SREs) play a key role in issue identification and
resolution. After an issue is reported, SREs come together in a virtual room
(collaboration platform) to triage the issue. While doing so, they leave behind
a wealth of information which can be used later for triaging similar issues.
However, usability of the conversations offer challenges due to them being i)
noisy and ii) unlabelled. This paper presents a novel approach for issue
artefact extraction from the noisy conversations with minimal labelled data. We
propose a combination of unsupervised and supervised model with minimum human
intervention that leverages domain knowledge to predict artefacts for a small
amount of conversation data and use that for fine-tuning an already pretrained
language model for artefact prediction on a large amount of conversation data.
Experimental results on our dataset show that the proposed ensemble of
unsupervised and supervised model is better than using either one of them
individually.


Abstract:
  Backpropagation through time (BPTT) is a technique of updating tuned
parameters within recurrent neural networks (RNNs). Several attempts at
creating such an algorithm have been made including: Nth Ordered Approximations
and Truncated-BPTT. These methods approximate the backpropagation gradients
under the assumption that the RNN only utilises short-term dependencies. This
is an acceptable assumption to make for the current state of artificial neural
networks. As RNNs become more advanced, a shift towards influence by long-term
dependencies is likely. Thus, a new method for backpropagation is required. We
propose using the 'discrete forward sensitivity equation' and a variant of it
for single and multiple interacting recurrent loops respectively. This solution
is exact and also allows the network's parameters to vary between each
subsequent step, however it does require the computation of a Jacobian.


Abstract:
  In this paper, we present the performance analysis of mixed RF/FSO system
with multiple relays. To select the best relay, we adopt partial relay
selection with outdated CSI wherein we investigate the effect of the temporal
correlation of the channels. Unlike the vast majority of work, we introduce the
impairments to the relays and the destination and we compare the performance
against conventional RF relaying systems. We further derive the expressions of
the outage probability and the ergodic capacity as well as the bounds to unpack
engineering insights into the system robustness.


Abstract:
  A group is called strongly bounded, if the speed with which it is generated
by finitely many conjugacy classes has a positive, lower bound only dependent
on the number of the conjugacy classes in question rather than the actual
conjugacy classes. Earlier papers by Kedra, Libman and Martin and myself have
shown that this is a property common to split Chevalley groups defined using an
irreducible root system of rank at least $2$ and the ring of all S-algebraic
integers and that the situation is dependent on the number theory of $R$ for
${\rm Sp}_4$ and $G_2.$ In this paper, we will show that ${\rm SL}_2(R)$ is
also strongly bounded for $R$ the ring of all S-algebraic integers in a number
field $K$ with $R$ having infinitely many units and will give a complete
account of the existence of small conjugacy classes generating ${\rm SL}_2(R)$
in terms of the prime factorization of the rational primes $2$ and $3$ in $R.$


Abstract:
  The news ecosystem has become increasingly complex, encompassing a wide range
of sources with varying levels of trustworthiness, and with public commentary
giving different spins to the same stories. In this paper, we present a
multi-platform measurement of this ecosystem. We compile a list of 1,073 news
websites and extract posts from four Web communities (Twitter, Reddit, 4chan,
and Gab) that contain URLs from these sources. This yields a dataset of 38M
posts containing 15M news URLs, spanning almost three years.
  We study the data along several axes, assessing the trustworthiness of shared
news, designing a method to group news articles into stories, analyzing these
stories are discussed and measuring the influence various Web communities have
in that. Our analysis shows that different communities discuss different types
of news, with polarized communities like Gab and /r/The_Donald subreddit
disproportionately referencing untrustworthy sources. We also find that fringe
communities often have a disproportionate influence on other platforms w.r.t.
pushing narratives around certain news, for example about political elections,
immigration, or foreign policy.


Abstract:
  Due to their small size and unique properties, single-molecule electronics
have long seen research interest from experimentalists and theoreticians alike.
From a theoretical standpoint, modeling these systems using electronic
structure theory can be difficult due to the importance of electron correlation
in the determination of molecular properties, and this electron correlation can
be computationally expensive to consider, particularly multiconfigurational
correlation energy. In this work, we develop a new approach for the study of
single-molecule electronic systems, denoted NEGF-MCPDFT, which combines
multiconfiguration pair-density functional theory (MC-PDFT) with the
non-equilibrium Green's function formalism (NEGF). The use of MC-PDFT with NEGF
allows for the efficient inclusion of both static and dynamic electron
correlation in the description of the junction's electronic structure. CASSCF
wave functions are used as references in the MC-PDFT calculation, and like with
any active space method, effort must be made to determine the proper orbital
character to include in the active space. We perform conductance and
transmission calculations on a series of alkanes (predominantly
single-configurational character) and benzyne (multiconfigurational character),
exploring the role that active space selection has on the computed results. For
the alkane junctions explored (where dynamic electron correlation dominates),
the MCPDFT-NEGF results agree well with DFT-NEGF results. For the benzyne
junction (which has significant static correlation), we see clear differences
in the MCPDFT-NEGF and DFT-NEGF results, and evidence that NEGF-MCPDFT is
capturing additional electron correlation effects beyond those provided by the
PBE functional.


Abstract:
  The insights revealed from process mining heavily rely on the quality of
event logs. Activities extracted from healthcare information systems with the
free-text nature may lead to inconsistent labels. Such inconsistency would then
lead to redundancy of activity labels, which refer to labels that have
different syntax but share the same behaviours. The identifications of these
labels from data-driven process discovery are difficult and rely heavily on
resource-intensive human review. Existing work achieves low accuracy either
redundant activity labels are in low occurrence frequency or the existence of
numerical data values as attributes in event logs. However, these phenomena are
commonly observed in healthcare information systems. In this paper, we propose
an approach to detect redundant activity labels using control-flow relations
and numerical data values from event logs. Natural Language Processing is also
integrated into our method to assess semantic similarity between labels, which
provides users with additional insights. We have evaluated our approach through
synthetic logs generated from the real-life Sepsis log and a case study using
the MIMIC-III data set. The results demonstrate that our approach can
successfully detect redundant activity labels. This approach can add value to
the preprocessing step to generate more representative event logs for process
mining tasks in the healthcare domain.


Abstract:
  Quantum computing's greatest challenge is scaling up. Several decades ago,
classical computers faced the same problem and a single solution emerged:
very-large-scale integration using silicon. Today's silicon chips consist of
billions of field-effect transistors (FinFETs) in which current flow along the
fin-shaped channel is controlled by wrap-around gates. The semiconductor
industry currently employs fins of sub-10$\,$nm width, small enough for quantum
applications: at low temperature, an electron or hole can be trapped under the
gate and serve as a spin qubit. An attractive benefit of silicon's advantageous
scaling properties is that quantum hardware and its classical control circuitry
can be integrated in the same package. This, however, requires qubit operation
at temperatures greater than 1$\,$K where the cooling is sufficient to overcome
the heat dissipation. Here, we demonstrate that a silicon FinFET is an
excellent host for spin qubits that operate even above 4$\,$K. We achieve fast
electrical control of hole spins with driving frequencies up to 150$\,$MHz and
single-qubit gate fidelities at the fault-tolerance threshold. The number of
spin rotations before coherence is lost at these "hot" temperatures already
matches or exceeds values on hole spin qubits at mK temperatures. While our
devices feature both industry compatibility and quality, they are fabricated in
a flexible and agile way to accelerate their development. This work paves the
way towards large-scale integration of all-electrical and ultrafast spin
qubits.


Abstract:
  We present the first interferometric blind HI survey of the Fornax galaxy
cluster, which covers an area of 15 deg$^2$ out to the cluster $R_{vir}$. The
survey has a resolution of 67''x95'' and 6.6 km$s^{-1}$ with a 3$\sigma$
sensitivity of N(HI)~2x10$^{19}$ cm$^{-2}$ and MHI 2x10$^7$ M$_\odot$.
  We detect 16 galaxies out of 200 spectroscopically confirmed Fornax cluster
members. The detections cover ~3 orders of magnitude in HI mass, from 8x10$^6$
to 1.5x10$^{10}$ M$_\odot$. They avoid the central, virialised region of the
cluster both on the sky and in projected phase-space, showing that they are
recent arrivals and that, in Fornax, HI is lost within a crossing time, ~2 Gyr.
Half of these galaxies exhibit a disturbed HI morphology, including several
cases of asymmetries, tails, offsets between HI and optical centres, and a case
of a truncated HI disc suggesting that they have been interacting within or on
their way to Fornax. Our HI detections are HI-poorer and form stars at a lower
rate than non-cluster galaxies in the same $M_\star$ range. Low mass galaxies
are more strongly affected throughout their infall towards the cluster. The
MHI/$M_\star$ ratio of Fornax galaxies is comparable to that in the Virgo
cluster. At fixed $M_\star$, our HI detections follow the non-cluster relation
between MHI and the star formation rate, and we argue that this implies that so
far they have lost their HI on a timescale $\gtrsim$1-2 Gyr. Deeper inside the
cluster HI removal is likely to proceed faster, as confirmed by a population of
HI-undetected but H$_2$-detected star-forming galaxies. Based on ALMA data, we
find a large scatter in H$_2$-to-HI mass ratio, with several galaxies showing
an unusually high ratio that is probably caused by faster HI removal.
  We identify an HI-rich subgroup of possible interacting galaxies dominated by
NGC 1365, where pre-processing is likey to have taken place.


Abstract:
  Extreme-mass ratio inspirals (EMRIs), compact binaries with small mass ratios
$\epsilon\ll 1$, will be important sources for low-frequency gravitational wave
detectors. Almost all EMRIs will evolve through important transient orbital
$r\theta$ resonances, which will enhance or diminish their gravitational wave
flux, thereby affecting the phase evolution of the waveforms at
$O(\epsilon^{1/2})$ relative to leading order. While modeling the local
gravitational self-force (GSF) during resonances is essential for generating
accurate EMRI waveforms, so far the full GSF has not been calculated for an
$r\theta$-resonant orbit owing to computational demands of the problem. As a
first step we employ a simpler model, calculating the scalar self-force (SSF)
along $r\theta$-resonant geodesics in Kerr spacetime. We demonstrate two ways
of calculating the $r\theta$-resonant SSF (and likely GSF), with one method
leaving the radial and polar motions initially independent as if the geodesic
is nonresonant. We illustrate results by calculating the SSF along geodesics
defined by three $r\theta$-resonant ratios (1:3, 1:2, 2:3). We show how the SSF
and averaged evolution of the orbital constants vary with the initial phase at
which an EMRI enters resonance. We then use our SSF data to test a previously
proposed integrability conjecture, which argues that conservative effects
vanish at adiabatic order during resonances. We find prominent contributions
from the conservative SSF to the secular evolution of the Carter constant,
$\langle \dot{\mathcal{Q}}\rangle$, but these nonvanishing contributions are on
the order of, or less than, the estimated uncertainties of our self-force
results. The uncertainties come from residual, incomplete removal of the
singular field in the regularization process. Higher order regularization
parameters, once available, will allow definitive tests of the integrability
conjecture.


Abstract:
  We propose new Riemannian preconditioned algorithms for low-rank tensor
completion via the polyadic decomposition of a tensor. These algorithms exploit
a non-Euclidean metric on the product space of the factor matrices of the
low-rank tensor in the polyadic decomposition form. This new metric is designed
using an approximation of the diagonal blocks of the Hessian of the tensor
completion cost function, thus has a preconditioning effect on these
algorithms. We prove that the proposed Riemannian gradient descent algorithm
globally converges to a stationary point of the tensor completion problem, with
convergence rate estimates using the $\L{}$ojasiewicz property. Numerical
results on synthetic and real-world data suggest that the proposed algorithms
are more efficient in memory and time compared to state-of-the-art algorithms.
Moreover, the proposed algorithms display a greater tolerance for overestimated
rank parameters in terms of the tensor recovery performance, thus enable a
flexible choice of the rank parameter.


Abstract:
  In three generations or more, we derive the oscillation probabilities of both
Dirac and Majorana neutrinos relativistically by using the Dirac equation. We
present various oscillation probabilities for including wrong-helicity
neutrinos, right-handed neutrinos, and anti-neutrinos. We summarize the
relations between these probabilities. As neutrinos have finite mass, there are
two components for each chirality corresponding to positive and negative
helicities. We show that the probability is different for each component even
if neutrinos have the same chirality. The probabilities derived by the
relativistic equation depend on not only the mass squared differences but also
the absolute masses of neutrinos. Besides, the new CP phases appear in the
probabilities of oscillations with chirality-flip. These new CP phases are
equivalent to the Majorana CP phases in the case of Majorana neutrinos. We
investigate the CP dependence of oscillation probabilities in vacuum. There are
no direct CP violation in $\nu_{\alpha}\leftrightarrow \nu_{\beta}^c$
oscillations even if the flavors, $\alpha$ and $\beta$, are different as in the
same as two generations. In other words, the difference between the
CP-conjugate probabilities vanishes. However, in three generations or more, the
sine terms of new CP phases appear in the probabilities in addition to the
cosine terms. This is different from the result obtained in two generations.
Furthermore, the zero-distance effect does not appear in our formulation.


Abstract:
  Living systems operate far from thermal equilibrium by converting the
chemical potential of ATP into mechanical work to achieve growth, replication
or locomotion. Given time series observations of intra-, inter- or
multicellular processes, a key challenge is to detect non-equilibrium behavior
and quantify the rate of free energy consumption. Obtaining reliable bounds on
energy consumption and entropy production directly from experimental data
remains difficult in practice as many degrees of freedom typically are hidden
to the observer, so that the accessible coarse-grained dynamics may not
obviously violate detailed balance. Here, we introduce a novel method for
bounding the entropy production of physical and living systems which uses only
the waiting time statistics of hidden Markov processes and hence can be
directly applied to experimental data. By determining a universal limiting
curve, we infer entropy production bounds from experimental data for gene
regulatory networks, mammalian behavioral dynamics and numerous other
biological processes. Further considering the asymptotic limit of increasingly
precise biological timers, we estimate the necessary entropic cost of heartbeat
regulation in humans, dogs and mice.


Abstract:
  Capturing interpretable variations has long been one of the goals in
disentanglement learning. However, unlike the independence assumption,
interpretability has rarely been exploited to encourage disentanglement in the
unsupervised setting. In this paper, we examine the interpretability of
disentangled representations by investigating two questions: where to be
interpreted and what to be interpreted? A latent code is easily to be
interpreted if it would consistently impact a certain subarea of the resulting
generated image. We thus propose to learn a spatial mask to localize the effect
of each individual latent dimension. On the other hand, interpretability
usually comes from latent dimensions that capture simple and basic variations
in data. We thus impose a perturbation on a certain dimension of the latent
code, and expect to identify the perturbation along this dimension from the
generated images so that the encoding of simple variations can be enforced.
Additionally, we develop an unsupervised model selection method, which
accumulates perceptual distance scores along axes in the latent space. On
various datasets, our models can learn high-quality disentangled
representations without supervision, showing the proposed modeling of
interpretability is an effective proxy for achieving unsupervised
disentanglement.


Abstract:
  The negatively charged nitrogen-vacancy (NV) center in nano- or micro-
diamonds has emerged as a promising magnetic field sensor, as a candidate for
hyper-polarizing paramagnetic species, as well as a tool for spin-mechanics at
the nanoscale. However, NV-doped diamonds are presently not straightforwardly
employable for these applications in a liquid or when levitating under
atmospheric pressures due to the random angular Brownian motion which tends to
rotate the NV quantization axis over the course of the measurments. Here, we
report on angle locking of the crystalline axis of a trapped micro-diamond
along an external magnetic field. Specifically, we use spin population
inversion after a ground state level crossing of the NV center to turn the
diamond into a diamagnet. The diamond crystalline axis naturally aligns to the
magnetic field with high precision and in the absence of micro-wave, offering
bright prospects for applications in biology and spin-mechanical platforms.


Abstract:
  In the past years, numerous methods have been introduced to reliably detect
digital face image manipulations. Lately, the generalizability of these schemes
has been questioned in particular with respect to image post-processing. Image
compression represents a post-processing which is frequently applied in diverse
biometric application scenarios. Severe compression might erase digital traces
of face image manipulation and hence hamper a reliable detection thereof. In
this work, the effects of image compression on face image manipulation
detection are analyzed. In particular, a case study on facial retouching
detection under the influence of image compression is presented. To this end,
ICAO-compliant subsets of two public face databases are used to automatically
create a database containing more than 9,000 retouched reference images
together with unconstrained probe images. Subsequently, reference images are
compressed applying JPEG and JPEG 2000 at compression levels recommended for
face image storage in electronic travel documents. Novel detection algorithms
utilizing texture descriptors and deep face representations are proposed and
evaluated in a single image and differential scenario. Results obtained from
challenging cross-database experiments in which the analyzed retouching
technique is unknown during training yield interesting findings: (1) most
competitive detection performance is achieved for differential scenarios
employing deep face representations; (2) image compression severely impacts the
performance of face image manipulation detection schemes based on texture
descriptors while methods utilizing deep face representations are found to be
highly robust; (3) in some cases, the application of image compression might as
well improve detection performance.


Abstract:
  We consider a system consisting of a server, which receives updates for $N$
files according to independent Poisson processes. The goal of the server is to
deliver the latest version of the files to the user through a parallel network
of $K$ caches. We consider an update received by the user successful, if the
user receives the same file version that is currently prevailing at the server.
We derive an analytical expression for information freshness at the user. We
observe that freshness for a file increases with increase in consolidation of
rates across caches. To solve the multi-cache problem, we first solve the
auxiliary problem of a single-cache system. We then rework this auxiliary
solution to our parallel-cache network by consolidating rates to single routes
as much as possible. This yields an approximate (sub-optimal) solution for the
original problem. We provide an upper bound on the gap between the sub-optimal
solution and the optimal solution. Numerical results show that the sub-optimal
policy closely approximates the optimal policy.


Abstract:
  Controlling the motion of macroscopic oscillators in the quantum regime has
been the subject of intense research in recent decades. In this direction,
opto-mechanical systems, where the motion of micro-objects is strongly coupled
with laser light radiation pressure, have had tremendous success. In
particular, the motion of levitating objects can be manipulated at the quantum
level thanks to their very high isolation from the environment under ultra-low
vacuum conditions. To enter the quantum regime, schemes using single long-lived
atomic spins, such as the electronic spin of nitrogen-vacancy (NV) centers in
diamond, coupled with levitating mechanical oscillators have been proposed. At
the single spin level, they offer the formidable prospect of transferring the
spins' inherent quantum nature to the oscillators, with foreseeable
far-reaching implications in quantum sensing and tests of quantum mechanics.
Adding the spin degrees of freedom to the experimentalists' toolbox would
enable access to a very rich playground at the crossroads between condensed
matter and atomic physics. We review recent experimental work in the field of
spin-mechanics that employ the interaction between trapped particles and
electronic spins in the solid state and discuss the challenges ahead. Our focus
is on the theoretical background close to the current experiments, as well as
on the experimental limits, that, once overcome, will enable these systems to
unleash their full potential.


Abstract:
  The young magnetically active solar-like stars are efficient generators of
ionizing radiation in the form of X-ray and Extreme UV (EUV) flux, stellar wind
and eruptive events. These outputs are the critical factors affecting
atmospheric escape and chemistry of (exo)planets around active stars. While
X-ray fluxes and surface magnetic fields can be derived from observations, the
EUV emission and wind mass fluxes, Coronal Mass Ejections and associated
Stellar Energetic Particle events cannot be directly observed. Here, we present
the results of a three-dimensional magnetohydrodynamic (MHD) model with inputs
constrained by spectropolarimetric data, HST/STIS Far UV, X-ray data data and
stellar magnetic maps reconstructed at two epochs separated by 11 months. The
simulations show that over the course of the year, the global stellar corona
had undergone a drastic transition from a simple dipole-like to a tilted dipole
with multipole field components, and thus, provided favorable conditions for
Corotating Interaction Events (CIRs) that drive strong shocks. The dynamic
pressure exerted by CIRs are 1300 times larger than those observed from the Sun
and can contribute to the atmospheric erosion of early Venus, Earth, Mars and
young Earth-like exoplanets. Our data-constrained MHD model provides the
framework to model coronal environments of G-M planet hosting dwarfs. The model
outputs can serve as a realistic input for exoplanetary atmospheric models to
evaluate the impact of stellar coronal emission, stellar winds and CIRs on
their atmospheric escape and chemistry that can be tested in the upcoming JWST
and ground-based observations.


Abstract:
  Now, the pre-training technique is ubiquitous in natural language processing
field. ProphetNet is a pre-training based natural language generation method
which shows powerful performance on English text summarization and question
generation tasks. In this paper, we extend ProphetNet into other domains and
languages, and present the ProphetNet family pre-training models, named
ProphetNet-X, where X can be English, Chinese, Multi-lingual, and so on. We
pre-train a cross-lingual generation model ProphetNet-Multi, a Chinese
generation model ProphetNet-Zh, two open-domain dialog generation models
ProphetNet-Dialog-En and ProphetNet-Dialog-Zh. And also, we provide a PLG
(Programming Language Generation) model ProphetNet-Code to show the generation
performance besides NLG (Natural Language Generation) tasks. In our
experiments, ProphetNet-X models achieve new state-of-the-art performance on 10
benchmarks. All the models of ProphetNet-X share the same model structure,
which allows users to easily switch between different models. We make the code
and models publicly available, and we will keep updating more pre-training
models and finetuning scripts.


Abstract:
  Stakeholders make various types of decisions with respect to requirements,
design, management, and so on during the software development life cycle.
Nevertheless, these decisions are typically not well documented and classified
due to limited human resources, time, and budget. To this end, automatic
approaches provide a promising way. In this paper, we aimed at automatically
classifying decisions into five types to help stakeholders better document and
understand decisions. First, we collected a dataset from the Hibernate
developer mailing list. We then experimented and evaluated 270 configurations
regarding feature selection, feature extraction techniques, and machine
learning classifiers to seek the best configuration for classifying decisions.
Especially, we applied an ensemble learning method and constructed ensemble
classifiers to compare the performance between ensemble classifiers and base
classifiers. Our experiment results show that (1) feature selection can
decently improve the classification results; (2) ensemble classifiers can
outperform base classifiers provided that ensemble classifiers are well
constructed; (3) BoW + 50% features selected by feature selection with an
ensemble classifier that combines Na\"ive Bayes (NB), Logistic Regression (LR),
and Support Vector Machine (SVM) achieves the best classification result (with
a weighted precision of 0.750, a weighted recall of 0.739, and a weighted
F1-score of 0.727) among all the configurations. Our work can benefit various
types of stakeholders in software development through providing an automatic
approach for effectively classifying decisions into specific types that are
relevant to their interests.


Abstract:
  In this paper, we obtain dispersion relations corresponding to plane wave
solutions in various Lorentz-breaking extensions of gravity with dimensions 3,
4, 5 and 6. We demonstrate that these dispersion relations display an usual
Lorentz-invariant mode when the corresponding additive term involves higher
derivatives.


Abstract:
  Despite the substantial progress of active learning for image recognition,
there still lacks an instance-level active learning method specified for object
detection. In this paper, we propose Multiple Instance Active Object Detection
(MI-AOD), to select the most informative images for detector training by
observing instance-level uncertainty. MI-AOD defines an instance uncertainty
learning module, which leverages the discrepancy of two adversarial instance
classifiers trained on the labeled set to predict instance uncertainty of the
unlabeled set. MI-AOD treats unlabeled images as instance bags and feature
anchors in images as instances, and estimates the image uncertainty by
re-weighting instances in a multiple instance learning (MIL) fashion. Iterative
instance uncertainty learning and re-weighting facilitate suppressing noisy
instances, toward bridging the gap between instance uncertainty and image-level
uncertainty. Experiments validate that MI-AOD sets a solid baseline for
instance-level active learning. On commonly used object detection datasets,
MI-AOD outperforms state-of-the-art methods with significant margins,
particularly when the labeled sets are small. Code is available at
https://github.com/yuantn/MI-AOD.


Abstract:
  Parallel one-dimensional semiconductor channels connected by a
superconducting strip constitute the core platform in several recent quantum
device proposals that rely e.g. on Andreev processes or topological effects. In
order to realize these proposals, the actual material systems must have high
crystalline purity and the coupling between the different elements should be
controllable in terms of their interfaces and geometry. We present a strategy
for synthesizing double InAs nanowires by the vapor-liquid-solid mechanism
using III-V molecular beam epitaxy. A superconducting layer is deposited onto
nanowires without breaking vacuum, ensuring pristine interfaces between the
superconductor and the two semiconductor nanowires. The method allows for a
high yield of merged as well as separate parallel nanowires, with full or
half-shell superconductor coatings. We demonstrate their utility in complex
quantum devices by electron transport measurements.


Abstract:
  We compute the Nielsen-Borsuk-Ulam number for any selfmap of $n-$torus,
$\mathbb{T}^n$, as well as any free involution $\tau$ in $\mathbb{T}^n$, with
$n \leqslant 3$. Finally, we conclude that the tori, $\mathbb{T}^1$,
$\mathbb{T}^2$ and $\mathbb{T}^3$, are Wecken spaces in Nielsen-Borsuk-Ulam
theory. Such a number is a lower bound for the minimal number of pair of points
such that $f(x)=f(\tau(x))$ in a given homotopy class of maps.


Abstract:
  Predictive coding networks (PCNs) are an influential model for information
processing in the brain. They have appealing theoretical interpretations and
offer a single mechanism that accounts for diverse perceptual phenomena of the
brain. On the other hand, backpropagation (BP) is commonly regarded to be the
most successful learning method in modern machine learning. Thus, it is
exciting that recent work formulates inference learning (IL) that trains PCNs
to approximate BP. However, there are several remaining critical issues: (i) IL
is an approximation to BP with unrealistic/non-trivial requirements, (ii) IL
approximates BP in single-step weight updates; whether it leads to the same
point as BP after the weight updates are conducted for more steps is unknown,
and (iii) IL is computationally significantly more costly than BP. To solve
these issues, a variant of IL that is strictly equivalent to BP in fully
connected networks has been proposed. In this work, we build on this result by
showing that it also holds for more complex architectures, namely,
convolutional neural networks and (many-to-one) recurrent neural networks. To
our knowledge, we are the first to show that a biologically plausible algorithm
is able to exactly replicate the accuracy of BP on such complex architectures,
bridging the existing gap between IL and BP, and setting an unprecedented
performance for PCNs, which can now be considered as efficient alternatives to
BP.


Abstract:
  Gamers Private Network (GPN) is a client/server technology that guarantees a
connection for online video games that is more reliable and lower latency than
a standard internet connection. Users of the GPN technology benefit from a
stable and high-quality gaming experience for online games, which are hosted
and played across the world. After transforming a massive volume of raw
networking data collected by WTFast, we have structured the cleaned data into a
special-purpose data warehouse and completed the extensive analysis using
machine learning and neural nets technologies, and business intelligence tools.
These analyses demonstrate the ability to predict and quantify changes in the
network and demonstrate the benefits gained from the use of a GPN for users
when connected to an online game session.


Abstract:
  Multimodal self-supervised learning is getting more and more attention as it
allows not only to train large networks without human supervision but also to
search and retrieve data across various modalities. In this context, this paper
proposes a self-supervised training framework that learns a common multimodal
embedding space that, in addition to sharing representations across different
modalities, enforces a grouping of semantically similar instances. To this end,
we extend the concept of instance-level contrastive learning with a multimodal
clustering step in the training pipeline to capture semantic similarities
across modalities. The resulting embedding space enables retrieval of samples
across all modalities, even from unseen datasets and different domains. To
evaluate our approach, we train our model on the HowTo100M dataset and evaluate
its zero-shot retrieval capabilities in two challenging domains, namely
text-to-video retrieval, and temporal action localization, showing
state-of-the-art results on four different datasets.


Abstract:
  With the overwhelming popularity of Knowledge Graphs (KGs), researchers have
poured attention to link prediction to fill in missing facts for a long time.
However, they mainly focus on link prediction on binary relational data, where
facts are usually represented as triples in the form of (head entity, relation,
tail entity). In practice, n-ary relational facts are also ubiquitous. When
encountering such facts, existing studies usually decompose them into triples
by introducing a multitude of auxiliary virtual entities and additional
triples. These conversions result in the complexity of carrying out link
prediction on n-ary relational data. It has even proven that they may cause
loss of structure information. To overcome these problems, in this paper, we
represent each n-ary relational fact as a set of its role and role-value pairs.
We then propose a method called NaLP to conduct link prediction on n-ary
relational data, which explicitly models the relatedness of all the role and
role-value pairs in an n-ary relational fact. We further extend NaLP by
introducing type constraints of roles and role-values without any external
type-specific supervision, and proposing a more reasonable negative sampling
mechanism. Experimental results validate the effectiveness and merits of the
proposed methods.


Abstract:
  A \emph{fair competition}, based on the concept of envy-freeness, is a
non-eliminating competition where each contestant (team or individual player)
may not play against all other contestants, but the total difficulty for each
contestant is the same: the sum of the initial rankings of the opponents for
each contestant is the same. Similar to other non-eliminating competitions like
the Round-robin competition or the Swiss-system competition, the winner of the
fair competition is the contestant who wins the most games. The {\sc Fair
Non-Eliminating Tournament} ({\sc Fair-NET}) problem can be used to schedule
fair competitions whose infrastructure is known. In the {\sc Fair-NET} problem,
we are given an infrastructure of a tournament represented by a graph $G$ and
the initial rankings of the contestants represented by a multiset of integers
$S$. The objective is to decide whether $G$ is \emph{$S$-fair}, i.e., there
exists an assignment of the contestants to the vertices of $G$ such that the
sum of the rankings of the neighbors of each contestant in $G$ is the same
constant $k\in\mathbb{N}$. We initiate a study of the classical and
parameterized complexity of {\sc Fair-NET} with respect to several central
structural parameters motivated by real world scenarios, thereby presenting a
comprehensive picture of it.


Abstract:
  Kinematic numerators of Yang-Mills scattering amplitudes possess a rich Lie
algebraic structure that suggest the existence of a hidden infinite-dimensional
kinematic algebra. Explicitly realizing such a kinematic algebra is a
longstanding open problem that only has had partial success for simple helicity
sectors. In past work, we introduced a framework using tensor currents and
fusion rules to generate BCJ numerators of a special subsector of NMHV
amplitudes in Yang-Mills theory. Here we enlarge the scope and explicitly
realize a kinematic algebra for all NMHV amplitudes. Master numerators are
obtained directly from the algebraic rules and through commutators and
kinematic Jacobi identities other numerators can be generated. Inspecting the
output of the algebra, we conjecture a closed-form expression for the master
BCJ numerator up to any multiplicity. We also introduce a new method, based on
group algebra of the permutation group, to solve for the generalized gauge
freedom of BCJ numerators. It uses the recently introduced binary BCJ relations
to provide a complete set of NMHV kinematic numerators that consist of pure
gauge.


Abstract:
  In this article, we study the complexity involved in the computation of the
reach in arbitrary dimension and in particular the computation of the critical
spherical curvature points of an arbitrary algebraic variety. We present
properties of the critical spherical curvature points as well as an algorithm
for computing it.


Abstract:
  We have used B, V time series photometry collected with the Large Binocular
Telescope to undertake the first study of variable stars in the Milky Way
ultra-faint dwarf (UFD) satellites, Pisces II and Pegasus III. In Pisces II we
have identified a RRab star, one confirmed and a candidate SX Phoenicis star
and, a variable with uncertain classification. In Pegasus III we confirmed the
variability of two sources: an RRab star and a variable with uncertain
classification, similar to the case found in Pisces II. Using the
intensity-averaged apparent magnitude of the bona-fide RRab star in each galaxy
we estimate distance moduli of (m - M)0= 21.22 \pm 0.14 mag (d= 175 \pm 11 kpc)
and 21.21 \pm 0.23 mag (d=174 \pm 18 kpc) for Pisces II and Pegasus III,
respectively. Tests performed to disentangle the actual nature of variables
with an uncertain classification led us to conclude that they most likely are
bright, long period and very metal poor RRab members of their respective hosts.
This may indicate that Pisces II and Pegasus III contain a dominant old stellar
population (t>12 Gyr) with metallicity < [Fe=H] > -1.8 dex along with,
possibly, a minor, more metal-poor component, as supported by the V , B-V
color-magnitude diagrams of the two UFDs and their spectroscopically confirmed
members. The metallicity spread that we derived from our data sample is 0.4 dex
in both systems. Lastly, we built isodensity contour maps which do not reveal
any irregular shape, thus making the existence of a physical connection between
these UFDs unlikely.


Abstract:
  The human proteome is enriched in proteins that do not fold into a stable 3D
structure. These intrinsically disordered proteins (IDPs) spontaneously
fluctuate between a large number of configurations in their native form.
Remarkably, the disorder does not lead to dysfunction as with denatured folded
proteins. In fact, unlike denatured proteins, recent evidences strongly suggest
that multiple biological functions stem from such structural plasticity. Here,
focusing on the nanoscopic length-scale, we review the latest advances in IDP
research and discuss some of the future directions in this highly promising
field.


Abstract:
  Massive star formation occurs in the interior of giant molecular clouds (GMC)
and proceeds through many stages. In this work, we focus on massive young
stellar objects (MYSOs) and Ultra-Compact HII regions (UCHII), where the former
are enshrouded in dense envelopes of dust and gas, which the latter have begun
dispersing. By selecting a complete sample of MYSOs and UCHII from the Red MSX
Source (RMS) survey data base, we combine Planck and IRAS data and build their
Spectral Energy Distributions (SEDs). With these, we estimate the physical
properties (dust temperatures, mass, luminosity) of the sample. Because the RMS
database provides unique solar distances, it also allows investigating the
instantaneous Star Formation Efficiency (SFE) as a function of Galactocentric
radius. We find that the SFE increase between 2 and 4.5 kpc, where it reaches a
peak, likely in correspondence of the accumulation of molecular material at the
end of the Galactic bar. It then stays approximately constant up to 9 kpc,
after which it linearly declines, in agreement with predictions from
extragalactic studies. This behavior suggests the presence of a significant
amount of undetected molecular gas at R$_G$ $>$ 8 kpc. Finally we present
diagnostic colors that can be used to identify sites of massive star formation.


Abstract:
  The properties of the first-discovered interstellar object (ISO), 1I/2017
(`Oumuamua), differ from both Solar System asteroids and comets, casting doubt
on a protoplanetary disk origin. In this study, we investigate the possibility
that it formed with a substantial H2 ice component in the starless core of a
giant molecular cloud. While interstellar solid hydrogen has yet to be
detected, this constituent would explain a number of the ISO's properties. We
consider the relevant processes required to build decameter-sized, solid
hydrogen bodies and assess the plausibility of growth in various size regimes.
Via an energy balance argument, we find that the most severe barrier to
formation is the extremely low temperature required for the favorability of
molecular hydrogen ice. However, if deposition occurs, we find that the
turbulence within starless cores is conducive for growth into kilometer-sized
bodies on sufficiently short timescales. Then, we analyze mass loss in the
interstellar medium and determine the necessary size for a hydrogen object to
survive a journey to the Solar System as a function of ISO age. Finally, we
discuss the implications if the H2 explanation is correct, and we assess the
future prospects of ISO science. If hydrogen ice ISOs do exist, our
hypothesized formation pathway would require a small population of porous, 100
micron dust in a starless core region that has cooled to 2.8K via adiabatic
expansion of the surrounding gas and excellent shielding from electromagnetic
radiation and cosmic rays.


Abstract:
  Bojanczyk and Pilipczuk showed in their celebrated article "Definability
equals recognizability for graphs of bounded treewidth" (LICS 2016) that
monadic second-order logic can define tree-decompositions in graphs of bounded
treewidth. This raises the question whether such decompositions can already be
defined in first-order logic (FO).
  We start by introducing the notion of tree-decompositions of bounded span,
which restricts the diameter of the subtree consisting of the bags containing a
same node of the structure. Having a bounded span is a natural property of
tree-decompositions when dealing with FO, since equality of nodes cannot in
general be recovered in FO when it doesn't hold. In particular, it encompasses
the notion of domino tree-decompositions.
  We show that path-decompositions of bounded span are not FO-continuous, in
the sense that there exist arbitrarily FO-similar graphs of bounded pathwidth
which do not possess FO-similar path-decompositions of bounded span. Then, we
show that tree-decompositions of bounded span are not FO-continuous either.


Abstract:
  Electronic Health Records (EHR) have been heavily used in modern healthcare
systems for recording patients' admission information to hospitals. Many
data-driven approaches employ temporal features in EHR for predicting specific
diseases, readmission times, or diagnoses of patients. However, most existing
predictive models cannot fully utilize EHR data, due to an inherent lack of
labels in supervised training for some temporal events. Moreover, it is hard
for existing works to simultaneously provide generic and personalized
interpretability. To address these challenges, we first propose a hyperbolic
embedding method with information flow to pre-train medical code
representations in a hierarchical structure. We incorporate these pre-trained
representations into a graph neural network to detect disease complications,
and design a multi-level attention method to compute the contributions of
particular diseases and admissions, thus enhancing personalized
interpretability. We present a new hierarchy-enhanced historical prediction
proxy task in our self-supervised learning framework to fully utilize EHR data
and exploit medical domain knowledge. We conduct a comprehensive set of
experiments and case studies on widely used publicly available EHR datasets to
verify the effectiveness of our model. The results demonstrate our model's
strengths in both predictive tasks and interpretable abilities.


Abstract:
  Several lines of evidence suggest the Milky Way underwent a major merger at
z~2 with a galaxy known as Gaia-Sausage-Enceladus (GSE). Here we use H3 Survey
data to argue that GSE entered the Galaxy on a retrograde orbit based on a
population of highly retrograde stars with chemistry similar to the largely
radial GSE debris. We present the first tailored, high-resolution N-body
simulations of the merger. From a grid of ~500 simulations we find a GSE with
$M_{*}=5\times10^{8}\ M_{\odot}, M_{\rm{DM}}=2\times10^{11} M_{\odot}$ (a 2.5:1
total mass merger) best matches the H3 data. This simulation shows the
retrograde GSE stars are stripped from its outer disk early in the merger
before the orbit loses significant angular momentum. Despite being selected
purely on angular momenta and radial distributions, this simulation reproduces
and explains the following empirical phenomena: (i) the elongated, triaxial
shape of the inner halo (axis ratios $10:7.9:4.5$), whose major axis is at
~35{\deg} to the plane and connects GSE's apocenters, (ii) the Hercules-Aquila
Cloud & the Virgo Overdensity, which arise due to apocenter pile-up, (iii) the
2 Gyr lag between the quenching of GSE and the truncation of the age
distribution of the in-situ halo, which tracks the 2 Gyr gap between the first
and final GSE pericenters. We make the following predictions: (i) the inner
halo has a "double-break" density profile with breaks at both ~15-18 kpc and 30
kpc, coincident with the GSE apocenters, (ii) the outer halo has retrograde
streams containing ~10% of GSE stars awaiting discovery at >30 kpc. The
retrograde (radial) GSE debris originates from its outer (inner) disk --
exploiting this trend we reconstruct the stellar metallicity gradient of GSE
($-0.04\pm0.01$ dex $r_{\rm{50}}^{-1}$). These simulations imply GSE delivered
~20% of the Milky Way's present-day dark matter and ~50% of its stellar halo.
(ABRIDGED)


Abstract:
  The unitarity of quantum evolutions implies that the overlap between two
initial states does not change in time. This property is commonly believed to
explain the lack of state sensitivity in quantum theory, a feature that is
prevailing in classical chaotic systems. However, a distance between two points
in classical phase space is a completely different mathematical concept than an
overlap distance between two points in Hilbert space. There is a possibility
that state sensitivity in quantum theory can be uncovered with a help of some
other metric. Here we show that the recently introduced Weighted Bures Length
(WBL) achieves this task. In particular, we numerically study a cellular
automaton-like unitary evolution of N qubits, known as Rule 54, and apply WBL
to show that a single-qubit perturbation of a random initial state: (a) grows
linearly in time under the nearest neighbour interaction on a cycle, (b)
appears to grow exponentially in time under interaction given by a random
bipartite graph.


Abstract:
  The kagom\'e lattice is a fruitful source of novel physical states of matter,
including the quantum spin liquid and Dirac fermions. Here we report a
structural, thermodynamic, and transport study of the two-dimensional kagom\'e
metal-organic frameworks Ni_3(HIB)v2 and Cu_3(HIB)_2 (HIB = hexaiminobenzene).
Magnetization measurements yield Curie constants of 1.12 and 0.352 emu K mol
f.u.-1 Oe-1 respectively, close to the values expected for ideal S=1 and S=1/2
moments. Weiss temperatures of -20.3 K and -6.52 K, respectively, indicate
moderate to weak magnetic interactions. Electrical transport measurements
reveal that both materials are semiconducting, with gaps of Eg = 22.2 and 103
meV, respectively. Specific heat measurements reveal a large T-linear
contribution of {\gamma} = 148(4) mJ mol-f.u.-1 K-2 in Ni_3(HIB)_2 with only a
gradual upturn below T ~ 5 K and no evidence of a phase transition to an
ordered state down to T = 0.1 K. Cu_3(HIB)_2 also lacks evidence of a phase
transition above T = 0.1 K, with a substantial, field-dependent, magnetic
contribution below T ~ 5 K. Despite being superficially in agreement with
expectations of magnetic frustration and spin liquid physics, we are able to
explain these observations as arising due to known stacking disorder in these
materials. Our results further state the art of kagom\'e lattice physics,
especially in the rarely explored regime of semiconducting but not metallic
behavior.


Abstract:
  The increasing quantity of multi-omics data, such as methylomic and
transcriptomic profiles, collected on the same specimen, or even on the same
cell, provide a unique opportunity to explore the complex interactions that
define cell phenotype and govern cellular responses to perturbations. We
propose a network approach based on Gaussian Graphical Models (GGMs) that
facilitates the joint analysis of paired omics data. This method, called DRAGON
(Determining Regulatory Associations using Graphical models on multi-Omic
Networks), calibrates its parameters to achieve an optimal trade-off between
the network's complexity and estimation accuracy, while explicitly accounting
for the characteristics of each of the assessed omics layers. In simulation
studies, we show that DRAGON adapts to edge-density and feature-size
differences between the omics layers, which improves model inference and the
recovery of associations compared to state-of-the-art network inference using
GGMs. Next, we verified this performance gain on paired single-cell data, where
the transcriptome was assayed together with selected proteins. Finally, we used
DRAGON to study joint transcriptome-methylome data of breast cancer specimens.
In this context, we provide examples of how DRAGON can identify key molecular
mechanisms. For instance, we show how co-expression can be explained by
co-methylation and how DRAGON gives hints about the spatial organization of the
genome. DRAGON is available as open-source code in python through the Network
Zoo package (netZooPy v0.8; netzoo.github.io).


Abstract:
  In modern drug development, the broader availability of high-dimensional
observational data provides opportunities for scientist to explore subgroup
heterogeneity, especially when randomized clinical trials are unavailable due
to cost and ethical constraints. However, a common practice that naively
searches the subgroup with a high treatment level is often misleading due to
the "subgroup selection bias." More importantly, the nature of high-dimensional
observational data has further exacerbated the challenge of accurately
estimating the subgroup treatment effects. To resolve these issues, we provide
new inferential tools based on resampling to assess the replicability of
post-hoc identified subgroups from observational studies. Through careful
theoretical justification and extensive simulations, we show that our proposed
approach delivers asymptotically sharp confidence intervals and debiased
estimates for the selected subgroup treatment effects in the presence of
high-dimensional covariates. We further demonstrate the merit of the proposed
methods by analyzing the UK Biobank data.


Abstract:
  We present results of two pilot studies that investigated human error
behaviours with an error prone in-air gesture recognizer. During the studies,
users performed a small set of simple in-air gestures. In the first study,
these gestures were abstract. The second study associated concrete tasks with
each gesture. Interestingly, the error patterns observed in the two studies
were substantially different.


Abstract:
  The combination of deep neural networks and Differential Privacy has been of
increasing interest in recent years, as it offers important data protection
guarantees to the individuals of the training datasets used. However, using
Differential Privacy in the training of neural networks comes with a set of
shortcomings, like a decrease in validation accuracy and a significant increase
in the use of resources and time in training. In this paper, we examine
super-convergence as a way of greatly increasing training speed of
differentially private neural networks, addressing the shortcoming of high
training time and resource use. Super-convergence allows for acceleration in
network training using very high learning rates, and has been shown to achieve
models with high utility in orders of magnitude less training iterations than
conventional ways. Experiments in this paper show that this order-of-magnitude
speedup can also be seen when combining it with Differential Privacy, allowing
for higher validation accuracies in much fewer training iterations compared to
non-private, non-super convergent baseline models. Furthermore,
super-convergence is shown to improve the privacy guarantees of private models.


Abstract:
  We formulate and study the microscopic statistical mechanics of systems of
particles with exclusion statistics in a discrete one-body spectrum. The
statistical mechanics of these systems can be expressed in terms of effective
single-level grand partition functions obeying a generalization of the standard
thermodynamic exclusion statistics equation of state. We derive explicit
expressions for the thermodynamic potential in terms of microscopic cluster
coefficients and show that the mean occupation numbers of levels satisfy a
nesting relation involving a number of adjacent levels determined by the
exclusion parameter. We apply the formalism to the harmonic Calogero model and
point out a relation with the Ramanujan continued fraction identity and
appropriate generalizations.


Abstract:
  Let $\Bbbk$ be a field of characteristic 0. Let $X$ be a smooth complete
intersection over $k$ of dimension $n-k$ in the projective space
$\mathbf{P}^n_{k}$, for given positive integers $n$ and $k$. When
$k=\mathbb{C}$, Terasoma (\cite{Ter90}) and Konno (\cite{Ko91}) provided an
explicit representative (in terms of differential forms) of a basis for the
primitive middle-dimensional algebraic de Rham cohomology
$H_{dR,\operatorname{prim}}^{n-k}(X;\mathbb{C})$. Later Dimca constructed
another explicit representative of a basis of
$H_{dR,\operatorname{prim}}^{n-k}(X;\mathbb{C})$ in \cite{Dim95}. Moreover, he
proved that his representative gives the same cohomology class as the previous
representative of Terasoma and Konno. The goal of this article is to examine
the above two different approaches without assuming that $k=\mathbb{C}$ and
provide a similar comparison result for any field $k$. Dimca's argument depends
heavily on the condition $k=\mathbb{C}$ and our idea is to find appropriate
Cech-de Rham complexes and spectral sequences corresponding to those two
approaches, which work without restrictions on $k$.


Abstract:
  This report proposes a polyphonic sound event detection (SED) method for the
DCASE 2021 Challenge Task 4. The proposed SED model consists of two stages: a
mean-teacher model for providing target labels regarding weakly labeled or
unlabeled data and a self-training-based noisy student model for predicting
strong labels for sound events. The mean-teacher model, which is based on the
residual convolutional recurrent neural network (RCRNN) for the teacher and
student model, is first trained using all the training data from a weakly
labeled dataset, an unlabeled dataset, and a strongly labeled synthetic
dataset. Then, the trained mean-teacher model predicts the strong label to each
of the weakly labeled and unlabeled datasets, which is brought to the noisy
student model in the second stage of the proposed SED model. Here, the
structure of the noisy student model is identical to the RCRNN-based student
model of the mean-teacher model in the first stage. Then, it is self-trained by
adding feature noises, such as time-frequency shift, mixup, SpecAugment, and
dropout-based model noise. In addition, a semi-supervised loss function is
applied to train the noisy student model, which acts as label noise injection.
The performance of the proposed SED model is evaluated on the validation set of
the DCASE 2021 Challenge Task 4, and then, several ensemble models that combine
five-fold validation models with different hyperparameters of the
semi-supervised loss function are finally selected as our final models.


Abstract:
  Let $G$ be a graph on $n$ vertices. For $i\in \{0,1\}$ and a connected graph
$G$, a spanning forest $F$ of $G$ is called an $i$-perfect forest if every tree
in $F$ is an induced subgraph of $G$ and exactly $i$ vertices of $F$ have even
degree (including zero). A $i$-perfect forest of $G$ is proper if it has no
vertices of degree zero. Scott (2001) showed that every connected graph with
even number of vertices contains a (proper) 0-perfect forest. We prove that one
can find a 0-perfect forest with minimum number of edges in polynomial time,
but it is NP-hard to obtain a 0-perfect forest with maximum number of edges.
Moreover, we show that to decide whether $G$ has a 0-perfect forest with at
least $|V(G)|/2+k$ edges, where $k$ is the parameter, is W[1]-hard. We also
prove that for a prescribed edge $e$ of $G,$ it is NP-hard to obtain a
0-perfect forest containing $e,$ but one can decide if there existsa 0-perfect
forest not containing $e$ in polynomial time. It is easy to see that every
graph with odd number of vertices has a 1-perfect forest. It is not the case
for proper 1-perfect forests. We give a characterization of when a connected
graph has a proper 1-perfect forest.


Abstract:
  The mind-body problem is reviewed in the context of a non-technical account
of quantum information theory. The importance of clearly defining: `what is
physical?' is highlighted, since only then can we give meaning to the concept
`non-physical'. Physicality is defined in terms of interaction, which is in
turn defined to be a correlated exchange of information. This is asserted to be
the basis of any meaningful concept of epistemology. Hence, it is argued that a
non-physical entity can not `know' anything about the world. Information
transfer is then discussed in terms of quantum entanglement and an argument for
our perception of time is presented. It is then contended that the notion of
`mind' may be meaningfully discussed in the context of a quantum theoretic
framework.


Abstract:
  Several upper and lower bounds for the numerical radius of $2 \times 2$
operator matrices are developed which refine and generalize the earlier related
bounds. In particular, we show that if $B,C$ are bounded linear operators on a
complex Hilbert space, then \begin{eqnarray*}
  && \frac{1}{2}\max \left \{ \|B\|, \|C\| \right \}+\frac{1}{4} \left |
\|B+C^*\|-\|B-C^*\| \right |
  &&\leq w \left(\left[\begin{array}{cc}
  0 & B
  C& 0
  \end{array}\right]\right)\\ &&\leq \frac{1}{2} \max \left\{\|B\|,\|C\|\right
\}+\frac{1}{2}\max \left
\{r^{\frac{1}{2}}(|B||C^*|),r^{\frac{1}{2}}(|B^*||C|)\right\}, \end{eqnarray*}
where $w(.)$, $r(.)$ and $\|.\|$ are the numerical radius, spectral radius and
operator norm of a bounded linear operator, respectively. We also obtain
equality conditions for the numerical radius of the operator matrix
$\left[\begin{array}{cc}
  0 & B
  C& 0 \end{array}\right]$. As application of results obtained, we show that if
$B,C$ are self-adjoint operators then, $\max \Big \{\|B+C\|^2 , \|B-C\|^2
\Big\}\leq \left \|B^2+C^2 \right \|+2w(|B||C|). $


Abstract:
  Developing ways to affordably deliver broadband connectivity is one of the
major issues of our time. In challenging deployment locations with irregular
terrain, traditional Clear-Line-Of-Sight (CLOS) wireless links can be
uneconomical to deploy, as the number of required towers make infrastructure
investment unviable. With new research focusing on developing wireless
diffractive backhaul technologies to provide Non-Line-Of-Sight (NLOS) links,
this paper evaluates the engineering-economic implications. A Three-Dimensional
(3D) techno-economic assessment framework is developed, utilizing a combination
of remote sensing and viewshed geospatial techniques, in order to quantify the
impact of different wireless backhaul strategies. This framework is applied to
assess both Clear-Line-Of-Sight and diffractive Non-Line-Of-Sight strategies
for deployment in Peru, as well as the islands of Kalimantan and Papua, in
Indonesia. The results find that a hybrid strategy combining the use of
Clear-Line-Of-Sight and diffractive Non-Line-Of-Sight links produces a 9-45
percent cost-efficiency saving, relative to only using traditional
Clear-Line-Of-Sight wireless backhaul links.


Abstract:
  Measuring public opinion is a key focus during democratic elections, enabling
candidates to gauge their popularity and alter their campaign strategies
accordingly. Traditional survey polling remains the most popular estimation
technique, despite its cost and time intensity, measurement errors, lack of
real-time capabilities and lagged representation of public opinion. In recent
years, Twitter opinion mining has attempted to combat these issues. Despite
achieving promising results, it experiences its own set of shortcomings such as
an unrepresentative sample population and a lack of long term stability. This
paper aims to merge data from both these techniques using Bayesian data
assimilation to arrive at a more accurate estimate of true public opinion for
the Brexit referendum. This paper demonstrates the effectiveness of the
proposed approach using Twitter opinion data and survey data from trusted
pollsters. Firstly, the possible existence of a time gap of 16 days between the
two data sets is identified. This gap is subsequently incorporated into a
proposed assimilation architecture. This method was found to adequately
incorporate information from both sources and measure a strong upward trend in
Leave support leading up to the Brexit referendum. The proposed technique
provides useful estimates of true opinion, which is essential to future opinion
measurement and forecasting research.


Abstract:
  Identifying anomalies in multi-dimensional datasets is an important task in
many real-world applications. A special case arises when anomalies are occluded
in a small set of attributes, typically referred to as a subspace, and not
necessarily over the entire data space. In this paper, we propose a new
subspace analysis approach named Agglomerative Attribute Grouping (AAG) that
aims to address this challenge by searching for subspaces that are comprised of
highly correlative attributes. Such correlations among attributes represent a
systematic interaction among the attributes that can better reflect the
behavior of normal observations and hence can be used to improve the
identification of two particularly interesting types of abnormal data samples:
anomalies that are occluded in relatively small subsets of the attributes and
anomalies that represent a new data class. AAG relies on a novel
multi-attribute measure, which is derived from information theory measures of
partitions, for evaluating the "information distance" between groups of data
attributes. To determine the set of subspaces to use, AAG applies a variation
of the well-known agglomerative clustering algorithm with the proposed
multi-attribute measure as the underlying distance function. Finally, the set
of subspaces is used in an ensemble for anomaly detection. Extensive evaluation
demonstrates that, in the vast majority of cases, the proposed AAG method (i)
outperforms classical and state-of-the-art subspace analysis methods when used
in anomaly detection ensembles, and (ii) generates fewer subspaces with a fewer
number of attributes each (on average), thus resulting in a faster training
time for the anomaly detection ensemble. Furthermore, in contrast to existing
methods, the proposed AAG method does not require any tuning of parameters.


Abstract:
  We present a new and versatile implementation of rapid and localized
immunohistochemical staining of tissue sections. Immunohistochemistry (IHC)
allows to detect specific proteins on tissue sections and comprises a sequence
of specific biochemical reactions. For the rapid implementation of IHC, we
fabricated horizontally oriented microfluidic probes (MFP) with functionally
designed apertures to enable square and circular footprints, which we employ to
locally expose a tissue to time-optimized sequences of different biochemicals.


Abstract:
  A subalgebra S of a Leibniz algebra L is called self-idealizing in L if it
coincides with its idealizer IL(S). In this paper we study the structure of
Leibniz algebras, whose subalgebras are either ideals or self-idealizing.


Abstract:
  High-precision stellar analyses require hydrodynamic 3D modeling. Such models
predict changes across stellar disks of spectral line shapes, asymmetries, and
wavelength shifts. For testing models in stars other than the Sun, spatially
resolved observations are feasible from differential spectroscopy during
exoplanet transits, retrieving spectra of stellar surface segments that
successively become hidden behind the transiting planet, as shown in Papers I,
II, and III. Synthetic high-resolution spectra over extended spectral regions
are now available from 3D models. Similar to other ab initio simulations, these
data contain patterns that have not been specifically modeled but may be
revealed after analyses analogous to those of a large volume of observations.
From five 3D models spanning T=3964-6726K (approx. spectral types K8V-F3V),
synthetic spectra at hyper-high resolution (R>1,000,000) were analyzed.
Selected FeI and FeII lines at various positions across stellar disks were
searched for patterns between different lines in the same star and for similar
lines between different stars. Such patterns are identified for representative
photospheric lines of different strengths, excitation potential, and ionization
level, encoding the hydrodynamic 3D structure. Line profiles and bisectors are
shown for various stars at different positions across stellar disks. Absolute
convective wavelength shifts are obtained as differences to 1D models, where
such shifts do not occur. Observable relationships for line properties are
retrieved from realistically complex synthetic spectra. Such patterns may also
test very detailed 3D modeling, including non-LTE effects. While present
results are obtained at hyper-high spectral resolution, the subsequent Paper V
examines their practical observability at realistically lower resolutions, and
in the presence of noise.


Abstract:
  We investigate whether certain non-classical communication channels can be
simulated by a classical channel with a given number of states and a given
`amount' of noise. It is proved that any noisy quantum channel can be simulated
by a corresponding classical channel with `the same amount' of noise. Classical
simulations of general probabilistic channels are also studied.


Abstract:
  In this paper, we focus on removing interference of motion blur by the
derivation of motion blur invariants.Unlike earlier work, we don't restore any
blurred image. Based on geometric moment and mathematical model of motion blur,
we prove that geometric moments of blurred image and original image are
linearly related. Depending on this property, we can analyse whether an
existing moment-based feature is invariant to motion blur. Surprisingly, we
find some geometric moment invariants are invariants to not only spatial
transform but also motion blur. Meanwhile, we test invariance and robustness of
these invariants using synthetic and real blur image datasets. And the results
show these invariants outperform some widely used blur moment invariants and
non-moment image features in image retrieval, classification and template
matching.


Abstract:
  Neural Networks require large amounts of memory and compute to process high
resolution images, even when only a small part of the image is actually
informative for the task at hand. We propose a method based on a differentiable
Top-K operator to select the most relevant parts of the input to efficiently
process high resolution images. Our method may be interfaced with any
downstream neural network, is able to aggregate information from different
patches in a flexible way, and allows the whole model to be trained end-to-end
using backpropagation. We show results for traffic sign recognition,
inter-patch relationship reasoning, and fine-grained recognition without using
object/part bounding box annotations during training.