Abstract:
  In this paper, we study a class of revenue management problems where the
decision maker aims to maximize the total revenue subject to budget constraints
on multiple type of resources over a finite horizon. At each time, a new
order/customer/bid is revealed with a request of some resource(s) and a reward,
and the decision maker needs to either accept or reject the order. Upon the
acceptance of the order, the resource request must be satisfied and the
associated revenue (reward) can be collected. We consider a stochastic setting
where all the orders are i.i.d. sampled, i.e., the reward-request pair at each
time is drawn from an unknown distribution. The formulation contains many
classic applications such as the quantity-based network revenue management
problem and the Adwords problem. We consider \textit{regret} as the performance
measure defined by the gap between the optimal objective value of the
certainty-equivalent linear program (LP) and the expected revenue obtained by
the online algorithm. We show that under a finite support assumption on the
underlying distribution and a nondegeneracy assumption on the underlying LP,
the classic LP-based adaptive algorithm achieves a problem-dependent regret
upper bound that involves several parameters related to the underlying LP but
is not dependent on the horizon/number of time periods. In this sense, our
result improves upon the previous logarithmic regret bound in (Jasin 2015) and
highlights that the adaptive algorithm incurs a revenue loss independent on the
horizon even without the knowledge of the underlying distribution (Jasin and
Kumar, 2012) and against the strong benchmark of the certainty equivalent LP.


Abstract:
  Henize 2-10 (He 2-10) is a nearby (D = 9 Mpc) starbursting blue compact dwarf
galaxy that boasts a high star formation rate and a low luminosity AGN. He 2-10
is also one of the first galaxies in which embedded superstar clusters (SSCs)
were discovered. SSCs are massive, compact star clusters that will impact their
host galaxies dramatically when their massive stars evolve. Here, we discuss
radio, submillimeter, and infrared observations of He 2-10 from 1.87 microns to
6 cm in high angular resolution (~0.3 arcsec), which allows us to disentangle
individual clusters from aggregate complexes as identified at lower resolution.
These results indicate the importance of spatial resolution to characterize
SSCs, as low resolution studies of SSCs average over aggregate complexes that
may host SSCs at different stages of evolution. We explore the thermal,
non-thermal, and dust emission associated with the clusters along with dense
molecular tracers to construct a holistic review of the natal SSCs that have
yet to dramatically disrupt their parent molecular clouds. We assess the
production rate of ionizing photons, extinction, total mass, and the star
formation efficiency associated with the clusters. Notably, we find that the
star formation efficiency for the some of the natal clusters is high (>70%),
which suggests that these clusters could remain bound even after the gas is
dispersed from the system from stellar feedback mechanisms. If they remain
bound, these SSCs could survive to become objects indistinguishable from
globular clusters.


Abstract:
  We expose a hidden scaling symmetry of the Navier-Stokes equations in the
limit of vanishing viscosity, which stems from dynamical space-time rescaling
around suitably defined Lagrangian scaling centers. At a dynamical level, the
hidden symmetry projects solutions which differ up to Galilean invariance and
global temporal scaling onto the same representative flow. At a statistical
level, this projection repairs the scale invariance, which is broken by
intermittency in the original formulation. Following previous work by the first
author, we here postulate and substantiate with numerics that hidden symmetry
statistically holds in the inertial interval of fully developed turbulence. We
show that this symmetry accounts for the scale-invariance of a certain class of
observables, in particular, the Kolmogorov multipliers.


Abstract:
  We analyze complex multiplication for Jacobians of curves of genus 3, as well
as the resulting Shimura class groups and their subgroups corresponding to
Galois conjugation over the reflex field. We combine our results with numerical
methods to find CM fields $K$ for which there exist both hyperelliptic and
non-hyperelliptic curves whose Jacobian has complex multiplication by
$\mathbb{Z}_K$. More precisely, we find all sextic CM fields $K$ in the LMFDB
for which (heuristically) Jacobians of both types with CM by $\mathbb{Z}_K$
exist. There turn out to be 14 such fields among the 547,156 sextic CM fields
that the LMFDB contains. We determine invariants of the corresponding curves,
and in the simplest case we also give an explicit defining equation.


Abstract:
  Weakly supervised semantic segmentation produces a pixel-level localization
from a classifier, but it is likely to restrict its focus to a small
discriminative region of the target object. AdvCAM is an attribution map of an
image that is manipulated to increase the classification score. This
manipulation is realized in an anti-adversarial manner, which perturbs the
images along pixel gradients in the opposite direction from those used in an
adversarial attack. It forces regions initially considered not to be
discriminative to become involved in subsequent classifications, and produces
attribution maps that successively identify more regions of the target object.
In addition, we introduce a new regularization procedure that inhibits the
incorrect attribution of regions unrelated to the target object and limits the
attributions of the regions that already have high scores. On PASCAL VOC 2012
test images, we achieve mIoUs of 68.0 and 76.9 for weakly and semi-supervised
semantic segmentation respectively, which represent a new state-of-the-art.


Abstract:
  In recent years the interest in segmentation has been growing, being used in
a wide range of applications such as fraud detection, anomaly detection in
public health and intrusion detection. We present an ablation study of
FgSegNet_v2, analysing its three stages: (i) Encoder, (ii) Feature Pooling
Module and (iii) Decoder. The result of this study is a proposal of a variation
of the aforementioned method that surpasses state of the art results. Three
datasets are used for testing: CDNet2014, SBI2015 and CityScapes. In CDNet2014
we got an overall improvement compared to the state of the art, mainly in the
LowFrameRate subset. The presented approach is promising as it produces
comparable results with the state of the art (SBI2015 and Cityscapes datasets)
in very different conditions, such as different lighting conditions.


Abstract:
  Despite the recent successes in computer vision, there remain new avenues to
explore. In this work, we propose a new dataset to investigate the effect of
self-occlusion on deep neural networks. With TEOS (The Effect of
Self-Occlusion), we propose a 3D blocks world dataset that focuses on the
geometric shape of 3D objects and their omnipresent challenge of
self-occlusion. We designed TEOS to investigate the role of self-occlusion in
the context of object classification. Even though remarkable progress has been
seen in object classification, self-occlusion is a challenge. In the
real-world, self-occlusion of 3D objects still presents significant challenges
for deep learning approaches. However, humans deal with this by deploying
complex strategies, for instance, by changing the viewpoint or manipulating the
scene to gather necessary information. With TEOS, we present a dataset of two
difficulty levels (L1 and L2 ), containing 36 and 12 objects, respectively. We
provide 738 uniformly sampled views of each object, their mask, object and
camera position, orientation, amount of self-occlusion, as well as the CAD
model of each object. We present baseline evaluations with five well-known
classification deep neural networks and show that TEOS poses a significant
challenge for all of them. The dataset, as well as the pre-trained models, are
made publicly available for the scientific community under
https://nvision2.data.eecs.yorku.ca/TEOS.


Abstract:
  Mutual interplay between the electronic degrees of freedom in solids, such as
charge, spin, orbital, sublattice, and bond degrees of freedom, is a source of
cross-correlated phenomena with unconventional electronic ordered states. Such
degrees of freedom can be described by four types of multipoles (electric,
magnetic, magnetic toroidal, and electric toroidal) in a unified way, which
enable us to tightly connect the microscopic degrees of freedom with
macroscopic physical responses in a transparent manner. We complete a
classification of the multipoles in all 122 magnetic point groups based on the
group theory. The classification is useful to identify potentially active
multipoles not only in ordinary ferromagnetic and antiferromagnetic orderings
but also in exotic orderings breaking time-reversal symmetry, e.g., a
loop-current state. Moreover, the classification gives an insight into the
microscopic origin of the cross-correlated responses and quantum transports. By
analyzing response functions up to the second order, we summarize the
indispensable multipole moments for various responses, such as the linear
magnetoelectric, piezoelectric, and elastic responses, and the nonlinear
conductivity and Nernst coefficient. Our results highly promote a further
discovery of functional multiferroic materials, guided by the bottom-up
material design based on the symmetry-adapted multipoles.


Abstract:
  We develop a class of algebraic interpretations for many-sorted and
higher-order term rewriting systems that takes type information into account.
Specifically, base-type terms are mapped to \emph{tuples} of natural numbers
and higher-order terms to functions between those tuples. Tuples may carry
information relevant to the type; for instance, a term of type $\mathsf{nat}$
may be associated to a pair $(\mathsf{cost}, \mathsf{size})$ representing its
evaluation cost and size. This class of interpretations results in a more
fine-grained notion of complexity than runtime or derivational complexity,
which makes it particularly useful to obtain complexity bounds for higher-order
rewriting systems. We show that rewriting systems compatible with tuple
interpretations admit finite bounds on derivation height. Furthermore, we
demonstrate how to mechanically construct tuple interpretations and how to
orient $\beta$ and $\eta$ reductions within our technique. Finally, we relate
our method to runtime complexity and prove that specific interpretation shapes
imply certain runtime complexity bounds.


Abstract:
  This paper explores the extent to which regular expressions (regexes) are
portable across programming languages. Many languages offer similar regex
syntaxes, and it would be natural to assume that regexes can be ported across
language boundaries. But can regexes be copy/pasted across language boundaries
while retaining their semantic and performance characteristics?
  In our survey of 158 professional software developers, most indicated that
they re-use regexes across language boundaries and about half reported that
they believe regexes are a universal language. We experimentally evaluated the
riskiness of this practice using a novel regex corpus -- 537,806 regexes from
193,524 projects written in JavaScript, Java, PHP, Python, Ruby, Go, Perl, and
Rust. Using our polyglot regex corpus, we explored the hitherto-unstudied regex
portability problems: logic errors due to semantic differences, and security
vulnerabilities due to performance differences.
  We report that developers' belief in a regex lingua franca is understandable
but unfounded. Though most regexes compile across language boundaries, 15%
exhibit semantic differences across languages and 10% exhibit performance
differences across languages. We explained these differences using regex
documentation, and further illuminate our findings by investigating regex
engine implementations. Along the way we found bugs in the regex engines of
JavaScript-V8, Python, Ruby, and Rust, and potential semantic and performance
regex bugs in thousands of modules.


Abstract:
  Quantum illumination (QI) theoretically promises up to a 6dB error-exponent
advantage in target detection over the best classical protocol. The advantage
is maximised by a regime which includes a very high background, which occurs
naturally when one considers microwave operation. Such a regime has well-known
practical limitations, though it is clear that, theoretically, knowledge of the
associated classical benchmark in the microwave is lacking. The requirement of
amplifiers for signal detection necessarily renders the optimal classical
protocol here different to that which is traditionally used, and only
applicable in the optical domain. In this work we outline what is the true
classical benchmark for microwave QI using coherent states, providing new
bounds on the error probability and closed formulae for the receiver operating
characteristic (ROC), for both optimal (based on quantum relative entropy) and
homodyne detection schemes. We also propose an alternative source generation
procedure based on coherent states which demonstrates potential to reach
classically optimal performances achievable in optical applications. We provide
the same bounds and measures for the performance of such a source and discuss
its potential utility in the future of room temperature quantum detection
schemes in the microwave.


Abstract:
  Modulating image restoration level aims to generate a restored image by
altering a factor that represents the restoration strength. Previous works
mainly focused on optimizing the mean squared reconstruction error, which
brings high reconstruction accuracy but lacks finer texture details. This paper
presents a Controllable Unet Generative Adversarial Network (CUGAN) to generate
high-frequency textures in the modulation tasks. CUGAN consists of two modules
-- base networks and condition networks. The base networks comprise a generator
and a discriminator. In the generator, we realize the interactive control of
restoration levels by tuning the weights of different features from different
scales in the Unet architecture. Moreover, we adaptively modulate the
intermediate features in the discriminator according to the severity of
degradations. The condition networks accept the condition vector (encoded
degradation information) as input, then generate modulation parameters for both
the generator and the discriminator. During testing, users can control the
output effects by tweaking the condition vector. We also provide a smooth
transition between GAN and MSE effects by a simple transition method. Extensive
experiments demonstrate that the proposed CUGAN achieves excellent performance
on image restoration modulation tasks.


Abstract:
  Localized patterns in singularly perturbed reaction-diffusion equations
typically consist of slow parts -- in which the associated solution follows an
orbit on a slow manifold in a reduced spatial dynamical system -- alternated by
fast excursions -- in which the solution jumps from one slow manifold to
another, or back to the original slow manifold. In this paper we consider the
existence and stability of stationary and traveling slow localized patterns
that do not exhibit such jumps, i.e. that are completely embedded in a slow
manifold of the singularly perturbed spatial dynamical system. These patterns
have rarely been considered in the literature, for two reasons: (i) in the
classical Gray-Scott/Gierer-Meinhardt type models that dominate the literature,
the flow on the slow manifold is typically linear and thus cannot exhibit
homoclinic pulse or heteroclinic front solutions; (ii) the slow manifolds
occurring in the literature are typically `vertical' -- i.e. given by $u \equiv
u_0$, where $u$ is the fast variable -- so that the stability problem is
determined by a simple (decoupled) scalar equation. The present research
concerns a general system of singularly perturbed reaction-diffusion equations
and is motivated by several explicit ecosystem models that do give rise to
non-vertical normally hyperbolic slow manifolds on which the flow may exhibit
both homoclinic and heteroclinic orbits -- that correspond to either stationary
or traveling localized patterns. The associated spectral stability problems are
at leading order given by a nonlinear, but scalar, eigenvalue problem with
Sturm-Liouville characteristics and we establish that homoclinic pulse patterns
are typically unstable, while heteroclinic fronts can either be stable or
unstable. However, we also show that homoclinic pulse patterns that are
asymptotically close to a heteroclinic cycle may be stable.


Abstract:
  The Maximum Mean Discrepancy (MMD) has been the state-of-the-art
nonparametric test for tackling the two-sample problem. Its statistic is given
by the difference in expectations of the witness function, a real-valued
function defined as a weighted sum of kernel evaluations on a set of basis
points. Typically the kernel is optimized on a training set, and hypothesis
testing is performed on a separate test set to avoid overfitting (i.e., control
type-I error). That is, the test set is used to simultaneously estimate the
expectations and define the basis points, while the training set only serves to
select the kernel and is discarded. In this work, we argue that this data
splitting scheme is overly conservative, and propose to use the training data
to also define the weights and the basis points for better data efficiency. We
show that 1) the new test is consistent and has a well-controlled type-I error;
2) the optimal witness function is given by a precision-weighted mean in the
reproducing kernel Hilbert space associated with the kernel, and is closely
related to kernel Fisher discriminant analysis; and 3) the test power of the
proposed test is comparable or exceeds that of the MMD and other modern tests,
as verified empirically on challenging synthetic and real problems (e.g., Higgs
data).


Abstract:
  Recent empirical work shows that inconsistent results based on choice of
hyperparameter optimization (HPO) configuration are a widespread problem in ML
research. When comparing two algorithms J and K searching one subspace can
yield the conclusion that J outperforms K, whereas searching another can entail
the opposite. In short, the way we choose hyperparameters can deceive us. We
provide a theoretical complement to this prior work, arguing that, to avoid
such deception, the process of drawing conclusions from HPO should be made more
rigorous. We call this process epistemic hyperparameter optimization (EHPO),
and put forth a logical framework to capture its semantics and how it can lead
to inconsistent conclusions about performance. Our framework enables us to
prove EHPO methods that are guaranteed to be defended against deception, given
bounded compute time budget t. We demonstrate our framework's utility by
proving and empirically validating a defended variant of random search.


Abstract:
  Unsupervised domain adaptive object detection aims to adapt detectors from a
labelled source domain to an unlabelled target domain. Most existing works take
a two-stage strategy that first generates region proposals and then detects
objects of interest, where adversarial learning is widely adopted to mitigate
the inter-domain discrepancy in both stages. However, adversarial learning may
impair the alignment of well-aligned samples as it merely aligns the global
distributions across domains. To address this issue, we design an
uncertainty-aware domain adaptation network (UaDAN) that introduces conditional
adversarial learning to align well-aligned and poorly-aligned samples
separately in different manners. Specifically, we design an uncertainty metric
that assesses the alignment of each sample and adjusts the strength of
adversarial learning for well-aligned and poorly-aligned samples adaptively. In
addition, we exploit the uncertainty metric to achieve curriculum learning that
first performs easier image-level alignment and then more difficult
instance-level alignment progressively. Extensive experiments over four
challenging domain adaptive object detection datasets show that UaDAN achieves
superior performance as compared with state-of-the-art methods.


Abstract:
  This review is dedicated to two-dimensional sigma models with flag manifold
target spaces, which are generalizations of the familiar $CP^{n-1}$ and
Grassmannian models. They naturally arise in the description of continuum
limits of spin chains, and their phase structure is sensitive to the values of
the topological angles, which are determined by the representations of spins in
the chain. Gapless phases can in certain cases be explained by the presence of
discrete 't Hooft anomalies in the continuum theory. We also discuss integrable
flag manifold sigma models, which provide a generalization of the theory of
integrable models with symmetric target spaces. These models, as well as their
deformations, have an alternative equivalent formulation as bosonic Gross-Neveu
models, which proves useful for demonstrating that the deformed geometries are
solutions of the renormalization group (Ricci flow) equations, as well as for
the analysis of anomalies and for describing potential couplings to fermions.


Abstract:
  Nanomechanical circuits for transverse acoustic waves promise to enable new
approaches to computing, precision biochemical sensing and many other
applications. However, progress is hampered by the lack of precise control of
the coupling between nanomechanical elements. Here, we demonstrate
virtual-phonon coupling between transverse mechanical elements, exploiting
tunnelling through a zero-mode acoustic barrier. This allows the construction
of large-scale nanomechanical circuits on a silicon chip, for which we develop
a new scalable fabrication technique. As example applications, we build
mode-selective acoustic mirrors with controllable reflectivity and demonstrate
acoustic spatial mode filtering. Our work paves the way towards applications
such as fully nanomechanical computer processors and distributed nanomechanical
sensors, and to explore the rich landscape of nonlinear nanomechanical
dynamics.


Abstract:
  In this paper, we analyze the feasibility of applying few-shot learning to
speech emotion recognition task (SER). The current speech emotion recognition
models work exceptionally well but fail when then input is multilingual.
Moreover, when training such models, the models' performance is suitable only
when the training corpus is vast. This availability of a big training corpus is
a significant problem when choosing a language that is not much popular or
obscure. We attempt to solve this challenge of multilingualism and lack of
available data by turning this problem into a few-shot learning problem. We
suggest relaxing the assumption that all N classes in an N-way K-shot problem
be new and define an N+F way problem where N and F are the number of emotion
classes and predefined fixed classes, respectively. We propose this
modification to the Model-Agnostic MetaLearning (MAML) algorithm to solve the
problem and call this new model F-MAML. This modification performs better than
the original MAML and outperforms on EmoFilm dataset.


Abstract:
  Recently, there has been a significant growth of interest in applying
software engineering techniques for the quality assurance of deep learning (DL)
systems. One popular direction is deep learning testing, where adversarial
examples (a.k.a.~bugs) of DL systems are found either by fuzzing or guided
search with the help of certain testing metrics. However, recent studies have
revealed that the commonly used neuron coverage metrics by existing DL testing
approaches are not correlated to model robustness. It is also not an effective
measurement on the confidence of the model robustness after testing. In this
work, we address this gap by proposing a novel testing framework called
Robustness-Oriented Testing (RobOT). A key part of RobOT is a quantitative
measurement on 1) the value of each test case in improving model robustness
(often via retraining), and 2) the convergence quality of the model robustness
improvement. RobOT utilizes the proposed metric to automatically generate test
cases valuable for improving model robustness. The proposed metric is also a
strong indicator on how well robustness improvement has converged through
testing. Experiments on multiple benchmark datasets confirm the effectiveness
and efficiency of RobOT in improving DL model robustness, with 67.02% increase
on the adversarial robustness that is 50.65% higher than the state-of-the-art
work DeepGini.


Abstract:
  Let $X$ be a compact Riemann surface of genus $g \geq 3$. We consider the
moduli space of holomorphic connections over $X$ and the moduli space of
logarithmic connections singular over a finite subset of $X$ with fixed
residues. We determine the Chow group of these moduli spaces. We compute the
global sections of the sheaves of differential operators on ample line bundles
and their symmetric powers over these moduli spaces, and show that they are
constant under certain condition. We show the Torelli type theorem for the
moduli space of logarithmic connections. We also describe the rational
connectedness of these moduli spaces.


Abstract:
  We develop a new approach to $L^{\infty}$-a priori estimates for degenerate
complex Monge-Amp\`ere equations on complex manifolds. It only relies on
compactness and envelopes properties of quasi-plurisubharmonic functions. Our
method allows one to obtain new and efficient proofs of several fundamental
results in K\"ahler geometry as we explain in this article. In a sequel we
shall explain how this approach also applies to the hermitian setting producing
new relative a priori bounds, as well as existence results.


Abstract:
  Convolutional Neural Networks (CNNs) use pooling to decrease the size of
activation maps. This process is crucial to increase the receptive fields and
to reduce computational requirements of subsequent convolutions. An important
feature of the pooling operation is the minimization of information loss, with
respect to the initial activation maps, without a significant impact on the
computation and memory overhead. To meet these requirements, we propose
SoftPool: a fast and efficient method for exponentially weighted activation
downsampling. Through experiments across a range of architectures and pooling
methods, we demonstrate that SoftPool can retain more information in the
reduced activation maps. This refined downsampling leads to improvements in a
CNN's classification accuracy. Experiments with pooling layer substitutions on
ImageNet1K show an increase in accuracy over both original architectures and
other pooling methods. We also test SoftPool on video datasets for action
recognition. Again, through the direct replacement of pooling layers, we
observe consistent performance improvements while computational loads and
memory requirements remain limited.


Abstract:
  We investigate coarse-grained models of suspended self-thermophoretic
microswimmers. Upon heating, the Janus spheres, with hemispheres made of
different materials, induce a heterogeneous local solvent temperature that
causes the self-phoretic particle propulsion.
  Starting from atomistic molecular dynamics simulations, we verify the
coarse-grained description of the fluid in terms of a local molecular
temperature field, and its role for the particle's thermophoretic
self-propulsion and hot Brownian motion.
  The latter is governed by effective nonequilibrium temperatures, which are
measured from simulations by confining the particle position and orientation.
They are theoretically shown to remain relevant for any further spatial
coarse-graining towards a hydrodynamic description of the entire suspension as
a homogeneous complex fluid.


Abstract:
  We use the Beyond Ultra-deep Frontier Fields and Legacy Observations
(BUFFALO) strong lensing image catalog of the merging galaxy cluster Abell 370
to obtain a mass model using the free-form lens inversion algorithm GRALE. The
improvement of the strong lensing data quality results in a lens plane rms of
only 0.45 arcsec, about a factor of two lower than that of our existing HFF v4
reconstruction. We attribute the improvement to spectroscopic data and use of
the full reprocessed HST mosaics. In our reconstructed mass model, we found
indications of three distinct mass features in Abell 370: (i) a $\sim 35$ kpc
offset between the northern BCG and the nearest mass peak, (ii) a $\sim 100$
kpc mass concentration of roughly critical density $\sim 250$ kpc east of the
main cluster, and (iii) a probable filament-like structure passing N-S through
the cluster. While (i) is present in some form in most publicly available
reconstructions spanning the range of modeling techniques: parametric, hybrid,
and free-form, (ii) and (iii) are recovered by only about half of the
reconstructions. We tested our hypothesis on the presence of the filament-like
structure by creating a synthetic cluster - Irtysh IIIc - mocking the situation
of a cluster with external mass. We also computed the source plane
magnification distributions. Using them we estimated the probabilities of
magnifications in the source plane, and scrutinized their redshift dependence.
Finally, we explored the lensing effects of Abell 370 on the luminosity
functions of sources at $z_s=9.0$, finding it consistent with published
results.


Abstract:
  We report the instrumentation of a new positron diffractometer that is
connected to the high-intensity positron source NEPOMUC. Crucial elements for
the adaption of the positron beam are presented, which include the magnetic
field termination, the optional transmission-type remoderator for brightness
enhancement and the electrostatic system for acceleration and beam optics. The
positron trajectories of the remoderated and the twofold remoderated beam have
been simulated to optimize the system, i.e. to obtain a coherent beam of small
diameter. Within a first beamtime we tuned the system and characterized the
direct beam. For the twofold remoderated beam of 10keV energy, we
experimentally observe a beam diameter of d < 1.3mm, which agrees well with the
simulation.


Abstract:
  Reducing cycle time is a recurrent concern in the field of business process
management. Depending on the process, various interventions may be triggered to
reduce the cycle time of a case, for example, using a faster shipping service
in an order-to-delivery process or giving a phone call to a customer to obtain
missing information rather than waiting passively. Each of these interventions
comes with a cost. This paper tackles the problem of determining if and when to
trigger a time-reducing intervention in a way that maximizes the total net
gain. The paper proposes a prescriptive process monitoring method that uses
orthogonal random forest models to estimate the causal effect of triggering a
time-reducing intervention for each ongoing case of a process. Based on this
causal effect estimate, the method triggers interventions according to a
user-defined policy. The method is evaluated on two real-life logs.


Abstract:
  In recent years, Hyperspectral Imaging (HSI) has become a powerful source for
reliable data in applications such as remote sensing, agriculture, and
biomedicine. However, hyperspectral images are highly data-dense and often
benefit from methods to reduce the number of spectral bands while retaining the
most useful information for a specific application. We propose a novel band
selection method to select a reduced set of wavelengths, obtained from an HSI
system in the context of image classification. Our approach consists of two
main steps: the first utilizes a filter-based approach to find relevant
spectral bands based on a collinearity analysis between a band and its
neighbors. This analysis helps to remove redundant bands and dramatically
reduces the search space. The second step applies a wrapper-based approach to
select bands from the reduced set based on their information entropy values,
and trains a compact Convolutional Neural Network (CNN) to evaluate the
performance of the current selection. We present classification results
obtained from our method and compare them to other feature selection methods on
two hyperspectral image datasets. Additionally, we use the original
hyperspectral data cube to simulate the process of using actual filters in a
multispectral imager. We show that our method produces more suitable results
for a multispectral sensor design.


Abstract:
  In this paper, we present a distributed algorithm for solving convex,
constraint-coupled, optimization problems over peer-to-peer networks. We
consider a network of processors that aim to cooperatively minimize the sum of
local cost functions, subject to individual constraints and to global coupling
constraints. The major assumption of this work is that the cost functions are
unknown and must be learned online. We propose a fully distributed algorithm,
based on a primal decomposition approach, that uses iteratively refined
data-driven estimations of the cost functions over the iterations. The
algorithm is scalable and maintains private information of agents. We prove
that, asymptotically, the distributed algorithm provides the optimal solution
of the problem even though the true cost functions are never used within the
algorithm. The analysis requires an in-depth exploration of the primal
decomposition approach and shows that the distributed algorithm can be thought
of as an epsilon-subgradient method applied to a suitable reformulation of the
original problem. Finally, numerical computations cor- roborate the theoretical
findings and show the efficacy of the proposed approach.


Abstract:
  Variational quantum algorithms (VQAs) are proposed to solve relevant
computational problems on near term quantum devices. Popular versions are
variational quantum eigensolvers (VQEs) and quantum approximate optimization
algorithms (QAOAs) that solve ground state problems from quantum chemistry and
binary optimization problems, respectively. They are based on the idea to use a
classical computer to train a parameterized quantum circuit. We show that the
corresponding classical optimization problems are NP-hard. Moreover, the
hardness is robust in the sense that for every polynomial time algorithm, there
exists instances for which the relative error resulting from the classical
optimization problem can be arbitrarily large, unless P = NP. Even for
classically tractable systems, composed of only logarithmically many qubits or
free fermions, we show that the optimization is NP-hard. This elucidates that
the classical optimization is intrinsically hard and does not merely inherit
the hardness from the ground state problem. Our analysis shows that the
training landscape can have many far from optimal persistent local minima. This
means gradient and higher order decent algorithms will generally converge to
far from optimal solutions.


Abstract:
  Video action recognition (VAR) is a primary task of video understanding, and
untrimmed videos are more common in real-life scenes. Untrimmed videos have
redundant and diverse clips containing contextual information, so sampling
dense clips is essential. Recently, some works attempt to train a generic model
to select the N most representative clips. However, it is difficult to model
the complex relations from intra-class clips and inter-class videos within a
single model and fixed selected number, and the entanglement of multiple
relations is also hard to explain. Thus, instead of "only look once", we argue
"divide and conquer" strategy will be more suitable in untrimmed VAR. Inspired
by the speed reading mechanism, we propose a simple yet effective clip-level
solution based on skim-scan techniques. Specifically, the proposed Skim-Scan
framework first skims the entire video and drops those uninformative and
misleading clips. For the remaining clips, it scans clips with diverse features
gradually to drop redundant clips but cover essential content. The above
strategies can adaptively select the necessary clips according to the
difficulty of the different videos. To trade off the computational complexity
and performance, we observe the similar statistical expression between
lightweight and heavy networks, thus it supports us to explore the combination
of them. Comprehensive experiments are performed on ActivityNet and mini-FCVID
datasets, and results demonstrate that our solution surpasses the
state-of-the-art performance in terms of both accuracy and efficiency.


Abstract:
  Superradiant scattering of linear spin $s=0,\pm 1,\pm 2$ fields on Kerr black
hole background is investigated in the time domain by integrating numerically
the homogeneous Teukolsky master equation. The applied numerical setup has
already been used in studying long time evolution and tail behavior of
electromagnetic and metric perturbations on rotating black hole background
[arXiv:1905.09082v3]. To have a clear setup the initial data is chosen to be of
the compact support, while to optimize superradiance the frequency of the
initial data is fine tuned. Our most important finding is that the rate of
superradiance strongly depends on the relative position of the (compact)
support of the initial data and the ergoregion. When they are well-separated
then only a modest -- in case of $s=0$ scalar fields negligible --
superradiance occurs, whereas it can get to be amplified significantly whenever
the support of the initial data and the ergoregion overlap.


Abstract:
  This paper considers how the motions of a telepresence robot moving
autonomously affect a person immersed in the robot through a head-mounted
display. In particular, we explore the preference, comfort, and naturalness of
elements of piecewise linear paths compared to the same elements on a smooth
path. In a user study, thirty-six subjects watched panoramic videos of three
different paths through a simulated museum in virtual reality and responded to
questionnaires regarding each path. Preference for a particular path was
influenced the most by comfort, forward speed, and characteristics of the
turns. Preference was also strongly associated with the users' perceived
naturalness, which was primarily determined by the ability to see salient
objects, the distance to the walls and objects, as well as the turns.
Participants favored the paths that had a one meter per second forward speed
and rated the path with the least amount of turns as the most comfortable


Abstract:
  This paper proposes a system for automatic surface volume monitoring from
time series of SkySat pushframe imagery. A specific challenge of building and
comparing large 3D models from SkySat data is to correct inconsistencies
between the camera models associated to the multiple views that are necessary
to cover the area at a given time, where these camera models are represented as
Rational Polynomial Cameras (RPCs). We address the problem by proposing a
date-wise RPC refinement, able to handle dynamic areas covered by sets of
partially overlapping views. The cameras are refined by means of a rotation
that compensates for errors due to inaccurate knowledge of the satellite
attitude. The refined RPCs are then used to reconstruct multiple consistent
Digital Surface Models (DSMs) from different stereo pairs at each date. RPC
refinement strengthens the consistency between the DSMs of each date, which is
extremely beneficial to accurately measure volumes in the 3D surface models.
The system is tested in a real case scenario, to monitor large coal stockpiles.
Our volume estimates are validated with measurements collected on site in the
same period of time.


Abstract:
  The authors have introduced a novel method for unsupervised anomaly detection
that utilises a newly introduced Memory Module in their paper. We validate the
authors claim that this helps improve performance by helping the network learn
prototypical patterns, and uses the learnt memory to reduce the representation
capacity of Convolutional Neural Networks. Further, we validate the efficacy of
two losses introduced by the authors, Separateness Loss and Compactness Loss
presented to increase the discriminative power of the memory items and the
deeply learned features. We test the efficacy with the help of t-SNE plots of
the memory items.


Abstract:
  The method of using Hopf algebras for calculating Feynman integrals developed
by Abreu et al. is applied to the two-loop non-planar on-shell diagram with
massless propagators and three external mass scales. We show that the existence
of the method of cut Feynman diagrams comprising of the coproduct, the first
entry condition and integrability condition that was found to be true for the
planar case also holds for the non-planar case; furthermore, the non-planar
symbol alphabet is the same as for the planar case. This is one of the main
results of this work, and they have been obtained by a systematic analysis of
the relevant cuts, using the symbolic manipulation codes HypExp and
PolyLogTools. The obtained result for the symbol is cross-checked by an
analysis of the known two-loop original Feynman integral result. In addition,
we also reconstruct the full result from the symbol. This is the other main
result in this paper.


Abstract:
  Internet banking (IB) continues to face security concerns arising from
illegal access to users accounts. Use of personal identification numbers (PIN)
as a single authentication method for IB users is prone to insecurities such as
phishing, hacking and shoulder surfing. Fingerprint matching (FPM) as an
alternative to PIN equally has a downside as fingerprints reside on individual
mobile devices. A survey we conducted from 170 IB respondents of 5 different
banks in Brunei established that majority (65%) of them preferred use of
biometric authentication methods. In this work, we propose a two-level
integrated authentication mechanism (2L-IAM). At the first level, the user logs
in to their IB portal using either PIN or FPM. At the second level, user is
authenticated by means of face recognition (FR) should they initiate a
transaction classified as sensitive. The merits of the introduced 2L-IAM are
3-fold: - (1) FR guarantees the identity of the rightful user irrespective of
the login device; (2) By classifying banking products sensitivity, the
sensitive transactions are more effectively secured; (3) It is accommodative of
different users authentication preferences. Adoption of this framework could
thus improve both users and banks experiences in terms of enhanced security and
service delivery respectively.


Abstract:
  Natural convection in porous media is a fundamental process for the long-term
storage of CO2 in deep saline aquifers. Typically, details of mass transfer in
porous media are inferred from the numerical solution of the volume-averaged
Darcy-Oberbeck-Boussinesq (DOB) equations, even though these equations do not
account for the microscopic properties of a porous medium. According to the DOB
equations, natural convection in a porous medium is uniquely determined by the
Rayleigh number. However, in contrast with experiments, DOB simulations yield a
linear scaling of the Sherwood number with the Rayleigh number (Ra) for high
values of Ra (Ra>>1,300). Here, we perform Direct Numerical Simulations (DNS),
fully resolving the flow field within the pores. We show that the boundary
layer thickness is determined by the pore size instead of the Rayleigh number,
as previously assumed. The mega- and proto- plume sizes increase with the pore
size. Our DNS results exhibit a nonlinear scaling of the Sherwood number at
high porosity, and for the same Rayleigh number, higher Sherwood numbers are
predicted by DNS at lower porosities. It can be concluded that the scaling of
the Sherwood number depends on the porosity and the pore-scale parameters,
which is consistent with experimental studies.


Abstract:
  This article investigates a novel electrophoretic molecular communication
mechanism that utilizes a time-varying electric field, which induces
time-varying molecule velocities and in turn improves communication
performance. For a sinusoidal field, we specify favorable signal parameters
(e.g., phase and frequency) that yield excellent communication link
performance. We also analytically derive an optimized field function by
formulating an appropriate cost function and solving the Euler-Lagrange
equation. In our setup, the field strength is proportional to the molecular
velocity; we verify this assumption by solving the Basset-Boussinesq-Oseen
equation for a given time-varying electric field (forcing function) and
examining its implications for practical physical parameterizations of the
system. Our analysis and Monte-Carlo simulation results demonstrate that the
proposed time-varying approach can significantly increase the number of
information-carrying molecules expected to be observed at the receiver and
reduce the bit-error probability compared to the constant field benchmark.


Abstract:
  Consider a random vector $\mathbf{y}=\mathbf{\Sigma}^{1/2}\mathbf{x}$, where
the $p$ elements of the vector $\mathbf{x}$ are i.i.d. real-valued random
variables with zero mean and finite fourth moment, and $\mathbf{\Sigma}^{1/2}$
is a deterministic $p\times p$ matrix such that the spectral norm of the
population correlation matrix $\mathbf{R}$ of $\mathbf{y}$ is uniformly
bounded. In this paper, we find that the log determinant of the sample
correlation matrix $\hat{\mathbf{R}}$ based on a sample of size $n$ from the
distribution of $\mathbf{y}$ satisfies a CLT (central limit theorem) for
$p/n\to \gamma\in (0, 1]$ and $p\leq n$. Explicit formulas for the asymptotic
mean and variance are provided. In case the mean of $\mathbf{y}$ is unknown, we
show that after recentering by the empirical mean the obtained CLT holds with a
shift in the asymptotic mean. This result is of independent interest in both
large dimensional random matrix theory and high-dimensional statistical
literature of large sample correlation matrices for non-normal data. At last,
the obtained findings are applied for testing of uncorrelatedness of $p$ random
variables. Surprisingly, in the null case $\mathbf{R}=\mathbf{I}$, the test
statistic becomes completely pivotal and the extensive simulations show that
the obtained CLT also holds if the moments of order four do not exist at all,
which conjectures a promising and robust test statistic for heavy-tailed
high-dimensional data.


Abstract:
  We investigate the non-equilibrium electronic structure and characteristic
time scales in a candidate excitonic insulator, Ta$_2$NiSe$_5$, using time- and
angle-resolved photoemission spectroscopy with a temporal resolution of 50 fs.
Following a strong photoexcitation, the band gap closes transiently within 100
fs, i.e., on a time scale faster than the typical lattice vibrational period.
Furthermore, we find that the characteristic time associated with the rise of
the photoemission intensity above the Fermi energy decreases with increasing
excitation strength, while the relaxation time of the electron population
towards equilibrium shows an opposite behaviour. We argue that these
experimental observations can be consistently explained by an excitonic origin
of the band gap in the material. The excitonic picture is supported by
microscopic calculations based on the non-equilibrium Green's function
formalism for an interacting two-band system. We interpret the speedup of the
rise time with fluence in terms of an enhanced scattering probability between
photo-excited electrons and excitons, leading to an initially faster decay of
the order parameter. We show that the inclusion of electron-phonon coupling at
a semi-classical level changes only the quantitative aspects of the proposed
dynamics, while the qualitative features remain the same. The experimental
observations and microscopic calculations allow us to develop a simple and
intuitive phenomenological model that captures the main dynamics after
photoexcitation in Ta$_2$NiSe$_5$.


Abstract:
  First attempts of prediction of the facial growth (FG) direction were made
over half of a century ago. Despite numerous attempts and elapsed time, a
satisfactory method has not been established yet and the problem still poses a
challenge for medical experts. To our knowledge, this paper is the first
Machine Learning approach to the prediction of FG direction. Conducted data
analysis reveals the inherent complexity of the problem and explains the
reasons of difficulty in FG direction prediction based on 2D X-ray images. To
perform growth forecasting, we employ a wide range of algorithms, from logistic
regression, through tree ensembles to neural networks and consider three,
slightly different, problem formulations. The resulting classification accuracy
varies between 71% and 75%.


Abstract:
  In this paper, we study a model of directed polymers in random environment,
where the environment region is a dynamical tube with its width growing
polynomially along with time. It can be viewed as an interpolation of the
pinning model and the classic directed polymer model. We prove weak coupling
limits for the directed polymers on tube in all dimensions, as the inverse
temperature vanishes in a suitable rate. Phase transitions among disorder
irrelevance, marginal relevance and disorder relevance are observed.


Abstract:
  A study of a complete cosmological model based on an asymmetric scalar
doublet represented by the classical and phantom scalar Higgs fields is carried
out. At the same time, the assumption about the nonnegativity of the expansion
rate of the Universe, which in some cases contradicts the complete system of
Einstein's equations, was removed. A closed system of dynamic equations
describing the evolution of the cosmological model is formulated, and the
dependence of the topology of the Einstein - Higgs hypersurface of the
5-dimensional phase space of the dynamical system that determines the global
properties of the cosmological model on the fundamental constants of the model
is investigated. A qualitative analysis of the dynamical system of the
corresponding cosmological model is carried out, asymptotic phase trajectories
are constructed, and the results of numerical modeling are presented,
illustrating various types of behavior of the cosmological model.


Abstract:
  Compact cold-atom sensors depend on vacuum technology. One of the major
limitations to miniaturizing these sensors are the active pumps -- typically
ion pumps -- required to sustain the low pressure needed for laser cooling.
Although passively pumped chambers have been proposed as a solution to this
problem, technical challenges have prevented successful operation at the levels
needed for cold-atom experiments. We present the first demonstration of a
vacuum package successfully independent of ion pumps for more than a week; our
vacuum package is capable of sustaining a cloud of cold atoms in a
magneto-optical trap (MOT) for greater than 200 days using only non-evaporable
getters and a rubidium dispenser. Measurements of the MOT lifetime indicate the
package maintains a pressure of better than $2\times10^{-7}$ Torr. This result
will significantly impact the development of compact atomic sensors, including
those sensitive to magnetic fields, where the absence of an ion pump will be
advantageous.


Abstract:
  This paper seeks to provide clues as to why experimental evidence for the
alignment of slender fibers in semi-dilute suspensions under shear flows does
not match theoretical predictions. This paper posits that the hydrodynamic
interactions between the different fibers that might be responsible for the
deviation from theory, can at least partially be modeled by the coupling
between Jeffery's equation and Stokes' equation. It is proposed that if the
initial data is slightly non-uniform, in that the probability distribution of
the orientation has small spacial variations, then there is feedback via
Stokes' equation that causes these non-uniformities to grow significantly in
short amounts of time, so that the standard uncoupled Jeffery's equation
becomes a poor predictor when the volume ratio of fibers to fluid is not
extremely low. This paper provides numerical evidence, involving spectral
analysis of the linearization of the perturbation equation, to support this
theory.
  This paper differs from the published version in that it contains a
corrogendum at the end.


Abstract:
  In this paper, we propose a generalization of Continuous Logic ([BBHU08])
where the distances take values in suitable co-quantales (in the way as it was
proposed in [Fla97]). By assuming suitable conditions (e.g., being
co-divisible, co-Girard and a V-domain), we provide, as test questions, a proof
of a version of the Tarski-Vaught test (Proposition 3.35) and Los Theorem
(Theorem 3.62) in our setting.


Abstract:
  Why is it that semidefinite relaxations have been so successful in numerous
applications in computer vision and robotics for solving non-convex
optimization problems involving rotations? In studying the empirical
performance we note that there are few failure cases reported in the
literature, in particular for estimation problems with a single rotation,
motivating us to gain further theoretical understanding.
  A general framework based on tools from algebraic geometry is introduced for
analyzing the power of semidefinite relaxations of problems with quadratic
objective functions and rotational constraints. Applications include
registration, hand-eye calibration and rotation averaging. We characterize the
extreme points, and show that there exist failure cases for which the
relaxation is not tight, even in the case of a single rotation. We also show
that some problem classes are always tight given an appropriate
parametrization. Our theoretical findings are accompanied with numerical
simulations, providing further evidence and understanding of the results.


Abstract:
  We combine the giant equilibration speedup provided by the swap Monte Carlo
algorithm to extensive multi-CPU molecular dynamics simulations to explore the
first ten decades of the equilibrium dynamics of supercooled liquids near the
experimental glass transition. We demonstrate in particular the emergence of a
power law in relaxation spectra, in quantitative agreement with the excess
wings observed experimentally. Detailed analysis of particle motion shows that
this power law directly originates from broadly distributed processes
experienced by rare, localised regions relaxing much before the bulk. We
explain why these wings appear in "excess" by constructing an empirical model
associating heterogeneous activated dynamics and dynamic facilitation, which
appear to be the two minimal physical ingredients revealed by our simulations.


Abstract:
  The goal of this article is to extend a theorem of Lurie \[
  \mathsf{Sh}_A (X) = \mathsf{Fun}(\mathsf{Exit}_A (X), \mathsf{S}) \]
representing constructible sheaves with values in $ \mathsf{S} $, the $ \infty
$-category of spaces, on a stratified space $ X $ with poset of strata $ A $,
as functors from the exit paths $ \infty $-category $ \mathsf{Exit}_A (X) $ to
$ \mathsf{S} $. Lurie's representation theorem works provided $ A $ satisfy the
ascending chain condition. This typically rules out infinite dimensional
examples of stratified space.
  Building on it and with the help of a stratified homotopy invariance theorem
from Haine, we show that when $ X $ is a nice enough $ A $-stratified space and
when $ A $ is itself stratified $ A_{\leq 0} \subset A_{\leq 1} \subset \cdots
\subset A $ by posets satisfying the ascending chain condition, \[
  \mathsf{Hyp}_A (X) = \mathsf{Fun}(\mathsf{Exit}_A(X), \mathsf{S}) \] the $
\infty $-category of $ A $-constructible hypersheaves on $ X $ is represented
by functors from the exit paths $ \infty $-category of $ X $.
  There are two types of nice stratified spaces on which this extended
representation theorem applies: conically stratified spaces and spaces that are
sequential colimits of conically stratified spaces. Examples of application
include the metric and the topological exponentials of a Fr\'echet manifold,
locally countable simplicial complexes and more generally, locally countable
cylindrically normal CW-complexes.


Abstract:
  We extend the Altmann-Hausen presentation of normal affine algebraic
C-varieties endowed with effective torus actions to the real setting. In
particular, we focus on actions of quasi-split real tori, in which case we
obtain a simpler presentation.


Abstract:
  We propose a novel formulation of the "effectiveness problem" in
communications, put forth by Shannon and Weaver in their seminal work [2], by
considering multiple agents communicating over a noisy channel in order to
achieve better coordination and cooperation in a multi-agent reinforcement
learning (MARL) framework. Specifically, we consider a multi-agent partially
observable Markov decision process (MA-POMDP), in which the agents, in addition
to interacting with the environment can also communicate with each other over a
noisy communication channel. The noisy communication channel is considered
explicitly as part of the dynamics of the environment and the message each
agent sends is part of the action that the agent can take. As a result, the
agents learn not only to collaborate with each other but also to communicate
"effectively" over a noisy channel. This framework generalizes both the
traditional communication problem, where the main goal is to convey a message
reliably over a noisy channel, and the "learning to communicate" framework that
has received recent attention in the MARL literature, where the underlying
communication channels are assumed to be error-free. We show via examples that
the joint policy learned using the proposed framework is superior to that where
the communication is considered separately from the underlying MA-POMDP. This
is a very powerful framework, which has many real world applications, from
autonomous vehicle planning to drone swarm control, and opens up the rich
toolbox of deep reinforcement learning for the design of multi-user
communication systems.


Abstract:
  Robotic manipulation of unknown objects is an important field of research.
Practical applications occur in many real-world settings where robots need to
interact with an unknown environment. We tackle the problem of reactive
grasping by proposing a method for unknown object tracking, grasp point
sampling and dynamic trajectory planning. Our object tracking method combines
Siamese Networks with an Iterative Closest Point approach for pointcloud
registration into a method for 6-DoF unknown object tracking. The method does
not require further training and is robust to noise and occlusion. We propose a
robotic manipulation system, which is able to grasp a wide variety of formerly
unseen objects and is robust against object perturbations and inferior grasping
points.


Abstract:
  We show that the flow on a Bers slice given by the Weil-Petersson gradient
vector field of renormalized volume is globally attracting to its fuchsian
basepoint.


Abstract:
  There are some shreds of evidence that social opinion polarization leads to
the breakup of the relationship, some in the scale of small communities, but
others can divide large organizations or even a nation. The legacy methodology
to answer the root cause of opinion polarization in the society commonly use
random sampling or questionnaire-based approach, which considered expensive in
term of time and money. On the contrary, the opportunity to employ big data
approach using social media data provides a rich source to investigate several
questions, such as: how social opinion polarization formed, dynamic social
network mechanisms during time-windows observation, identification of dominant
actors and communities. The power of the big data approach lies in the number
of data analyzed, where the more data involved in the process, the more
accurate to describe the population condition. Today, computing power is no
longer become an obstacle to process large-scale data, thus the observation of
the social opinion polarization process is possible.


Abstract:
  Recent applications in machine learning have renewed the interest of the
community in min-max optimization problems. While gradient-based optimization
methods are widely used to solve such problems, there are however many
scenarios where these techniques are not well-suited, or even not applicable
when the gradient is not accessible. We investigate the use of direct-search
methods that belong to a class of derivative-free techniques that only access
the objective function through an oracle. In this work, we design a novel
algorithm in the context of min-max saddle point games where one sequentially
updates the min and the max player. We prove convergence of this algorithm
under mild assumptions, where the objective of the max-player satisfies the
Polyak-\L{}ojasiewicz (PL) condition, while the min-player is characterized by
a nonconvex objective. Our method only assumes dynamically adjusted accurate
estimates of the oracle with a fixed probability. To the best of our knowledge,
our analysis is the first one to address the convergence of a direct-search
method for min-max objectives in a stochastic setting.


Abstract:
  Tensor networks provide an efficient approximation of operations involving
high dimensional tensors and have been extensively used in modelling quantum
many-body systems. More recently, supervised learning has been attempted with
tensor networks, primarily focused on tasks such as image classification. In
this work, we propose a novel formulation of tensor networks for supervised
image segmentation which allows them to operate on high resolution medical
images. We use the matrix product state (MPS) tensor network on non-overlapping
patches of a given input image to predict the segmentation mask by learning a
pixel-wise linear classification rule in a high dimensional space. The proposed
model is end-to-end trainable using backpropagation. It is implemented as a
Strided Tensor Network to reduce the parameter complexity. The performance of
the proposed method is evaluated on two public medical imaging datasets and
compared to relevant baselines. The evaluation shows that the strided tensor
network yields competitive performance compared to CNN-based models while using
fewer resources. Additionally, based on the experiments we discuss the
feasibility of using fully linear models for segmentation tasks.


Abstract:
  We propose a quantum algorithm based on ridge regression model, which get the
optimal fitting parameters w and a regularization hyperparameter {\alpha} by
analysing the training dataset. The algorithm consists of two subalgorithms.
One is generating predictive value for a new input, the way is to apply the
phase estimation algorithm to the initial state |Xi and apply the controlled
rotation to the eigenvalue register. The other is finding an optimal
regularization hyperparameter {\alpha} , the way is to apply the phase
estimation algorithm to the initial state |yi and apply the controlled rotation
to the eigenvalue register. The second subalgorithm can compute the whole
training dataset in parallel that improve the efficiency. Compared with the
classical ridge regression algorithm, our algorithm overcome multicollinearity
and overfitting. Moreover, it have exponentially faster. What's more, our
algorithm can deal with the non-sparse matrices in comparison to some existing
quantum algorithms and have slightly speedup than the existing quantum
counterpart. At present, the quantum algorithm has a wide range of application
and the proposed algorithm can be used as a subroutine of other quantum
algorithms.


Abstract:
  In his study of finite (Vassiliev's) knot invariants,Vogel introduced the
so-called universal parameters, belonging to the projective plane, which
particularly parameterize the simple Lie algebras by the Vogel's table.
Subsequently a number of quantities, such as some universal knot invariants,
(quantum) dimensions of simple Lie algebras, etc., have been represented in
terms of these parameters, i.e. in the universal form. We prove that at the
points from the Vogel's table all known universal quantum dimension formulae
are linearly resolvable, i.e. yield finite answers even if these points are
singular, provided one restricts them to the appropriate lines. We show, that
the same phenomenon takes place for another three distinguished points in
Vogel's plane - $E_{7\frac{1}{2}}, X_1,$ and $X_2$. We also examine the same
formulae on linear resolvability at the remaining 48 distinguished points in
Vogel's plane, which correspond to the so-called $Y$-objects, and discover that
there are three points among them, which are regular for all known quantum
dimension formulae. Two of them happen to be sharing a remarkable similarity
with the simple Lie algebras, namely, the universal formulae yield
integer-valued outputs (dimensions) at those points in the classical limit.


Abstract:
  An increasingly popular method for solving a constrained combinatorial
optimisation problem is to first convert it into a quadratic unconstrained
binary optimisation (QUBO) problem, and solve it using a standard QUBO solver.
However, this relaxation introduces hyper-parameters that balance the objective
and penalty terms for the constraints, and their chosen values significantly
impact performance. Hence, tuning these parameters is an important problem.
Existing generic hyper-parameter tuning methods require multiple expensive
calls to a QUBO solver, making them impractical for performance critical
applications when repeated solutions of similar combinatorial optimisation
problems are required. In this paper, we propose the QROSS method, in which we
build surrogate models of QUBO solvers via learning from solver data on a
collection of instances of a problem. In this way, we are able capture the
common structure of the instances and their interactions with the solver, and
produce good choices of penalty parameters with fewer number of calls to the
QUBO solver. We take the Traveling Salesman Problem (TSP) as a case study,
where we demonstrate that our method can find better solutions with fewer calls
to QUBO solver compared with conventional hyper-parameter tuning techniques.
Moreover, with simple adaptation methods, QROSS is shown to generalise well to
out-of-distribution datasets and different types of QUBO solvers.


Abstract:
  We develop a new machine learning algorithm, Via Machinae, to identify cold
stellar streams in data from the Gaia telescope. Via Machinae is based on
ANODE, a general method that uses conditional density estimation and sideband
interpolation to detect local overdensities in the data in a model agnostic
way. By applying ANODE to the positions, proper motions, and photometry of
stars observed by Gaia, Via Machinae obtains a collection of those stars deemed
most likely to belong to a stellar stream. We further apply an automated
line-finding method based on the Hough transform to search for line-like
features in patches of the sky. In this paper, we describe the Via Machinae
algorithm in detail and demonstrate our approach on the prominent stream GD-1.
Though some parts of the algorithm are tuned to increase sensitivity to cold
streams, the Via Machinae technique itself does not rely on astrophysical
assumptions, such as the potential of the Milky Way or stellar isochrones. This
flexibility suggests that it may have further applications in identifying other
anomalous structures within the Gaia dataset, for example debris flow and
globular clusters.


Abstract:
  While Landauer's Principle sets a lower bound for the work required for a
computation, that work is recoverable for efficient computations. However,
practical physical computers, such as modern digital computers or biochemical
systems, are subject to constraints that make them inefficient -- irreversibly
dissipating significant energy. Recent results show that the dissipation in
such systems is bounded by the nonreciprocity of the embedded computation. We
investigate the consequences of this bound for different types of memory,
showing that different memory devices are better suited for different
computations. This correspondence comes from the time-reversal symmetries of
the memory, which depend on whether information is stored positionally or
magnetically. This establishes that the time symmetries of the memory device
play an essential roll in determining energetics. The energetic consequences of
time symmetries are particularly pronounced in nearly deterministic
computations, where the cost of computing diverges as minus log of the error
rate. We identify the coefficient of that divergence as the dissipation
divergence. We find that the dissipation divergence may be zero for a
computation when implemented in one type of memory while it's maximal when
implemented with another. Given flexibility in the type of memory, the
dissipation divergence is bounded below by the average state compression of the
computation. Moreover, we show how to explicitly construct the memory to
achieve this minimal dissipation. As a result, we find that logically
reversible computations are indeed thermodynamically efficient, but logical
irreversibility comes at a much higher cost than previously anticipated.


Abstract:
  Recognizing relations between entities is a pivotal task of relational
learning. Learning relation representations from distantly-labeled datasets is
difficult because of the abundant label noise and complicated expressions in
human language. This paper aims to learn predictive, interpretable, and robust
relation representations from distantly-labeled data that are effective in
different settings, including supervised, distantly supervised, and few-shot
learning. Instead of solely relying on the supervision from noisy labels, we
propose to learn prototypes for each relation from contextual information to
best explore the intrinsic semantics of relations. Prototypes are
representations in the feature space abstracting the essential semantics of
relations between entities in sentences. We learn prototypes based on
objectives with clear geometric interpretation, where the prototypes are unit
vectors uniformly dispersed in a unit ball, and statement embeddings are
centered at the end of their corresponding prototype vectors on the surface of
the ball. This approach allows us to learn meaningful, interpretable prototypes
for the final classification. Results on several relation learning tasks show
that our model significantly outperforms the previous state-of-the-art models.
We further demonstrate the robustness of the encoder and the interpretability
of prototypes with extensive experiments.


Abstract:
  Pre-trained language models like BERT achieve superior performances in
various NLP tasks without explicit consideration of syntactic information.
Meanwhile, syntactic information has been proved to be crucial for the success
of NLP applications. However, how to incorporate the syntax trees effectively
and efficiently into pre-trained Transformers is still unsettled. In this
paper, we address this problem by proposing a novel framework named
Syntax-BERT. This framework works in a plug-and-play mode and is applicable to
an arbitrary pre-trained checkpoint based on Transformer architecture.
Experiments on various datasets of natural language understanding verify the
effectiveness of syntax trees and achieve consistent improvement over multiple
pre-trained models, including BERT, RoBERTa, and T5.


Abstract:
  We define and investigate versions of Silver and Mathias forcing with respect
to lower and upper density. We focus on properness, Axiom A, chain conditions,
preservation of cardinals and adding Cohen reals. We find rough forcings that
collapse 2^\omega to \omega, while others are surprisingly gentle. We also
study connections between regularity properties induced by these parametrized
forcing notions and the Baire property.


Abstract:
  Based on Hrushovski, Palac{\'i}n and Pillay's example [6], we produce a new
structure without the canonical base property, which is interpretable in
Baudisch's group. Said structure is, in particular, CM-trivial, and thus at the
lowest possible level of the ample hierarchy.


Abstract:
  In this paper we study quadratic forms which are universal when restricted to
almost prime inputs, establishing finiteness theorems akin to the
Conway--Schneeberger 15 theorem.


Abstract:
  Integration of data from multiple omics techniques is becoming increasingly
important in biomedical research. Due to non-uniformity and technical
limitations in omics platforms, such integrative analyses on multiple omics,
which we refer to as views, involve learning from incomplete observations with
various view-missing patterns. This is challenging because i) complex
interactions within and across observed views need to be properly addressed for
optimal predictive power and ii) observations with various view-missing
patterns need to be flexibly integrated. To address such challenges, we propose
a deep variational information bottleneck (IB) approach for incomplete
multi-view observations. Our method applies the IB framework on marginal and
joint representations of the observed views to focus on intra-view and
inter-view interactions that are relevant for the target. Most importantly, by
modeling the joint representations as a product of marginal representations, we
can efficiently learn from observed views with various view-missing patterns.
Experiments on real-world datasets show that our method consistently achieves
gain from data integration and outperforms state-of-the-art benchmarks.


Abstract:
  Chalcogenide phase change materials enable non-volatile, low-latency
storage-class memory. They are also being explored for new forms of computing
such as neuromorphic and in-memory computing. A key challenge, however, is the
temporal drift in the electrical resistance of the amorphous states that encode
data. Drift, caused by the spontaneous structural relaxation of the newly
recreated melt-quenched amorphous phase, has consistently been observed to have
a logarithmic dependence in time. Here, we show that this observation is valid
only in a certain observable timescale. Using threshold-switching voltage as
the measured variable, based on temperature-dependent and short timescale
electrical characterization, we experimentally measure the onset of drift. This
additional feature of the structural relaxation dynamics serves as a new
benchmark to appraise the different classical models to explain drift.


Abstract:
  A multi-prism lens (MPL) is a refractive x-ray lens with one-dimensional
focusing properties. If used as a pre-object collimator in a scanning system
for medical x-ray imaging, it reduces the divergence of the radiation and
improves on photon economy compared to a slit collimator. Potential advantages
include shorter acquisition times, a reduced tube loading, or improved
resolution. We present the first images acquired with an MPL in a prototype for
a scanning mammography system. The lens showed a gain of flux of 1.32 compared
to a slit collimator at equal resolution, or a gain in resolution of 1.31-1.44
at equal flux. We expect the gain of flux in a clinical set-up with an
optimized MPL and a custom-made absorption filter to reach 1.67, or 1.45-1.54
gain in resolution.


Abstract:
  There is a prevailing trend to study urban morphology quantitatively thanks
to the growing accessibility to various forms of spatial big data, increasing
computing power, and use cases benefiting from such information. The methods
developed up to now measure urban morphology with numerical indices describing
density, proportion, and mixture, but they do not directly represent
morphological features from the human's visual and intuitive perspective. We
take the first step to bridge the gap by proposing a deep learning-based
technique to automatically classify road networks into four classes on a visual
basis. The method is implemented by generating an image of the street network
(Colored Road Hierarchy Diagram), which we introduce in this paper, and
classifying it using a deep convolutional neural network (ResNet-34). The model
achieves an overall classification accuracy of 0.875. Nine cities around the
world are selected as the study areas with their road networks acquired from
OpenStreetMap. Latent subgroups among the cities are uncovered through
clustering on the percentage of each road network category. In the subsequent
part of the paper, we focus on the usability of such classification: we apply
our method in a case study of urban vitality prediction. An advanced tree-based
regression model (LightGBM) is for the first time designated to establish the
relationship between morphological indices and vitality indicators. The effect
of road network classification is found to be small but positively associated
with urban vitality. This work expands the toolkit of quantitative urban
morphology study with new techniques, supporting further studies in the future.


Abstract:
  The key procedure of haze image translation through adversarial training lies
in the disentanglement between the feature only involved in haze synthesis,
i.e.style feature, and the feature representing the invariant semantic content,
i.e. content feature. Previous methods separate content feature apart by
utilizing it to classify haze image during the training process. However, in
this paper we recognize the incompleteness of the content-style disentanglement
in such technical routine. The flawed style feature entangled with content
information inevitably leads the ill-rendering of the haze images. To address,
we propose a self-supervised style regression via stochastic linear
interpolation to reduce the content information in style feature. The ablative
experiments demonstrate the disentangling completeness and its superiority in
level-aware haze image synthesis. Moreover, the generated haze data are applied
in the testing generalization of vehicle detectors. Further study between
haze-level and detection performance shows that haze has obvious impact on the
generalization of the vehicle detectors and such performance degrading level is
linearly correlated to the haze-level, which, in turn, validates the
effectiveness of the proposed method.


Abstract:
  Establishing dense correspondences between a pair of images is an important
and general problem. However, dense flow estimation is often inaccurate in the
case of large displacements or homogeneous regions. For most applications and
down-stream tasks, such as pose estimation, image manipulation, or 3D
reconstruction, it is crucial to know when and where to trust the estimated
matches.
  In this work, we aim to estimate a dense flow field relating two images,
coupled with a robust pixel-wise confidence map indicating the reliability and
accuracy of the prediction. We develop a flexible probabilistic approach that
jointly learns the flow prediction and its uncertainty. In particular, we
parametrize the predictive distribution as a constrained mixture model,
ensuring better modelling of both accurate flow predictions and outliers.
Moreover, we develop an architecture and training strategy tailored for robust
and generalizable uncertainty prediction in the context of self-supervised
training. Our approach obtains state-of-the-art results on multiple challenging
geometric matching and optical flow datasets. We further validate the
usefulness of our probabilistic confidence estimation for the task of pose
estimation. Code and models are available at
https://github.com/PruneTruong/PDCNet.


Abstract:
  In this work, I studied the spectrum of scalar mesons and axial-vector mesons
via Dyson-Schwinger equation and Bethe-Salpeter equation approach in the
rainbow-ladder approximation. An interaction model with a repulsive term added
to the one used for the pseudoscalar and vector mesons is proposed. My results
are consistent with the experiment data and other model results, which shows
that this interaction model is effective for all the heavy, heavy-light, and
light scalar and axial-vector mesons.


Abstract:
  Osteoporosis is a common bone disease that occurs when the creation of new
bone does not keep up with the loss of old bone, resulting in increased
fracture risk. Adults over the age of 50 are especially at risk and see their
quality of life diminished because of limited mobility, which can lead to
isolation and depression. We are developing a robust screening method capable
of identifying individuals predisposed to hip fracture to address this clinical
challenge. The method uses finite element analysis and relies on segmented
computed tomography (CT) images of the hip. Presently, the segmentation of the
proximal femur requires manual input, which is a tedious task, prone to human
error, and severely limits the practicality of the method in a clinical
context. Here we present a novel approach for segmenting the proximal femur
that uses a deep convolutional neural network to produce accurate, automated,
robust, and fast segmentations of the femur from CT scans. The network
architecture is based on the renowned u-net, which consists of a downsampling
path to extract increasingly complex features of the input patch and an
upsampling path to convert the acquired low resolution image into a high
resolution one. Skipped connections allow us to recover critical spatial
information lost during downsampling. The model was trained on 30 manually
segmented CT images and was evaluated on 200 ground truth manual segmentations.
Our method delivers a mean Dice similarity coefficient (DSC) and 95th
percentile Hausdorff distance (HD95) of 0.990 and 0.981 mm, respectively.


Abstract:
  In this paper, we introduce an event-driven trading strategy that predicts
stock movements by detecting corporate events from news articles. Unlike
existing models that utilize textual features (e.g., bag-of-words) and
sentiments to directly make stock predictions, we consider corporate events as
the driving force behind stock movements and aim to profit from the temporary
stock mispricing that may occur when corporate events take place. The core of
the proposed strategy is a bi-level event detection model. The low-level event
detector identifies events' existences from each token, while the high-level
event detector incorporates the entire article's representation and the
low-level detected results to discover events at the article-level. We also
develop an elaborately-annotated dataset EDT for corporate event detection and
news-based stock prediction benchmark. EDT includes 9721 news articles with
token-level event labels as well as 303893 news articles with minute-level
timestamps and comprehensive stock price labels. Experiments on EDT indicate
that the proposed strategy outperforms all the baselines in winning rate,
excess returns over the market, and the average return on each transaction.


Abstract:
  In this note we give some variations on an extension of Caratheodory's
normality criterion due to Grahl and Nevo.


Abstract:
  Dirac and Lov\'{a}sz independently characterized the $3$-connected graphs
with no pair of vertex-disjoint cycles. Equivalently, they characterized all
$3$-connected graphs with no prism-minors. In this paper, we completely
characterize the $3$-connected graphs with an edge that is contained in the
union of no pair of vertex-disjoint cycles. As applications, we answer the
analogous questions for edge-disjoint cycles and for $4$-connected graphs and
we completely characterize the $3$-connected graphs with no prism-minor using a
specified edge.


Abstract:
  We identify a subclass of the regular commutative languages that is closed
under the iterated shuffle, or shuffle closure. In particular, it is
regularity-preserving on this subclass. This subclass contains the commutative
group languages and, for every alphabet $\Sigma$, the class
$\textbf{Com}^+(\Sigma^*)$ given by the ordered variety $\textbf{Com}^+$. Then,
we state a simple characterization when the iterated shuffle on finite
commutative languages gives a regular language again and state partial results
for aperiodic commutative languages. We also show that the aperiodic, or
star-free, commutative languages and the commutative group languages are closed
under projection.


Abstract:
  A comprehensive class of models is proposed that can be used for continuous,
binary, ordered categorical and count type responses. The difficulty of items
is described by difficulty functions, which replace the item difficulty
parameters that are typically used in item response models. They crucially
determine the response distribution and make the models very flexible with
regard to the range of distributions that are covered. The model class contains
several widely used models as the binary Rasch model and the graded response
model as special cases, allows for simplifications, and offers a distribution
free alternative to count type items. A major strength of the models is that
they can be used for mixed item formats, when different types of items are
combined to measure abilities or attitudes. It is an immediate consequence of
the comprehensive modeling approach that allows that difficulty functions
automatically adapt to the response distribution. Basic properties of the model
class are shown. Several real data sets are used to illustrate the flexibility
of the models.


Abstract:
  A new concept for the simultaneous detection of primary and secondary
scintillation in time projection chambers is proposed. Its core element is a
type of very-thick GEM structure supplied with transparent electrodes and
machined from a polyethylene naphthalate plate, a natural wavelength-shifter.
Such a device has good prospects for scalability and, by virtue of its genuine
optical properties, it can improve on the light collection efficiency, energy
threshold and resolution of conventional micropattern gas detectors. This,
together with the intrinsic radiopurity of its constituting elements, offers
advantages for noble gas and liquid based time projection chambers, used for
dark matter searches and neutrino experiments. Production, optical and
electrical characterization, and first measurements performed with the new
device are reported.


Abstract:
  We show that the (positive) zoom complexes, here called tree complexes, with
fairly natural morphisms, form a dual category to the category of positive
opetopes with contraction epimorphisms. We also show how this duality can be
slightly generalized to thicket complexes and opetopic cardinals.


Abstract:
  There are many approaches that use weak-supervision to train networks to
segment 2D images. By contrast, existing 3D approaches rely on full-supervision
of a subset of 2D slices of the 3D image volume. In this paper, we propose an
approach that is truly weakly-supervised in the sense that we only need to
provide a sparse set of 3D point on the surface of target objects, an easy task
that can be quickly done. We use the 3D points to deform a 3D template so that
it roughly matches the target object outlines and we introduce an architecture
that exploits the supervision provided by coarse template to train a network to
find accurate boundaries.
  We evaluate the performance of our approach on Computed Tomography (CT),
Magnetic Resonance Imagery (MRI) and Electron Microscopy (EM) image datasets.
We will show that it outperforms a more traditional approach to
weak-supervision in 3D at a reduced supervision cost.


Abstract:
  In numerical linear algebra, a well-established practice is to choose a norm
that exploits the structure of the problem at hand in order to optimize
accuracy or computational complexity. In numerical polynomial algebra, a single
norm (attributed to Weyl) dominates the literature. This article initiates the
use of $L_p$ norms for numerical algebraic geometry, with an emphasis on
$L_{\infty}$. This classical idea yields strong improvements in the analysis of
the number of steps performed by numerous iterative algorithms. In particular,
we exhibit three algorithms where, despite the complexity of computing
$L_{\infty}$-norm, the use of $L_p$-norms substantially reduces computational
complexity: a subdivision-based algorithm in real algebraic geometry for
computing the homology of semialgebraic sets, a well-known meshing algorithm in
computational geometry, and the computation of zeros of systems of complex
quadratic polynomials (a particular case of Smale's 17th problem).


Abstract:
  Knowledge distillation refers to a technique of transferring the knowledge
from a large learned model or an ensemble of learned models to a small model.
This method relies on access to the original training set, which might not
always be available. A possible solution is a data-free adversarial
distillation framework, which deploys a generative network to transfer the
teacher model's knowledge to the student model. However, the data generation
efficiency is low in the data-free adversarial distillation. We add an
activation regularizer and a virtual interpolation method to improve the data
generation efficiency. The activation regularizer enables the students to match
the teacher's predictions close to activation boundaries and decision
boundaries. The virtual interpolation method can generate virtual samples and
labels in-between decision boundaries. Our experiments show that our approach
surpasses state-of-the-art data-free distillation methods. The student model
can achieve 95.42% accuracy on CIFAR-10 and 77.05% accuracy on CIFAR-100
without any original training data. Our model's accuracy is 13.8% higher than
the state-of-the-art data-free method on CIFAR-100.


Abstract:
  High eccentricity migration is a possible formation channel for hot Jupiters.
However, in order for it to be consistent with the observed population of
planets, tides must circularize the orbits in less than $\approx$ a Myr. A
potential mechanism for such rapid circularization is the diffusive growth of
the tidally driven planetary f-mode. Such growth occurs if the f-mode's phase
at pericenter varies chaotically from one pericenter passage to the next.
Previous studies focused on the variation of the orbital period due to tidal
back-reaction on the orbit as the source of chaos. Here we show that nonlinear
mode interactions can also be an important source. Specifically, we show that
nonlinear interactions between a parent f-mode and daughter f-/p-modes induce
an energy-dependent shift in the oscillation frequency of the parent. This
frequency shift varies randomly from orbit to orbit because the parent's energy
varies. As a result, the parent's phase at pericenter varies randomly, which we
find can trigger it to grow diffusively. We show that the phase shift induced
by nonlinear mode interactions in fact dominates the shift induced by tidal
back-reaction and significantly lowers the one-kick energy threshold for
diffusive growth by about a factor of 5 compared to the linear theory's
prediction. Nonlinear interactions could thus enhance the formation rate of hot
Jupiters through the high-eccentricity migration channel and potentially
mitigate the discrepancy between the observed and predicted occurrence rates
for close-in gas giants as compared to those further from the star.


Abstract:
  We present an analysis of the environment of six QSO triplets at 1 $\lesssim$
z $\lesssim$ 1.5 by analyzing multiband (r,i,z, or g,r,i) images obtained with
Megacam at the CFHT telescope, aiming to investigate whether they are
associated or not with galaxy protoclusters. This was done by using photometric
redshifts trained using the high accuracy photometric redshifts of the
COSMOS2015 catalogue. To improve the quality of our photometric redshift
estimation, we included in our analysis near-infrared photometry (3.6 and 4.5
$\mu m$) from the unWISE survey available for our fields and the COSMOS survey.
This approach allowed us to obtain good photometric redshifts with dispersion,
as measured with the robust Sigma NMAD statistics of $\sim$ 0.04 for our six
fields. Our analysis setup was reproduced on lightcones constructed from the
Millennium Simulation data and the latest version of the L-GALAXIES
semi-analytic model to verify the protocluster detectability in such
conditions. The density field in a redshift slab containing each triplet was
then analyzed with a Gaussian kernel density estimator. We did not find any
significant evidence of the triplets inhabiting dense structures, such as a
massive galaxy cluster or protocluster.


Abstract:
  We define a categorical notion of cybernetic system as a dynamical
realisation of a generalized open game, along with a coherence condition. We
show that this notion captures a wide class of cybernetic systems in
computational neuroscience and statistical machine learning, exposes their
compositional structure, and gives an abstract justification for the
bidirectional structure empirically observed in cortical circuits. Our
construction is built on the observation that Bayesian updates compose
optically, a fact which we prove along the way, via a fibred category of
state-dependent stochastic channels.


Abstract:
  This document describes the details and the motivation behind a new dataset
we collected for the semi-supervised recognition challenge~\cite{semi-aves} at
the FGVC7 workshop at CVPR 2020. The dataset contains 1000 species of birds
sampled from the iNat-2018 dataset for a total of nearly 150k images. From this
collection, we sample a subset of classes and their labels, while adding the
images from the remaining classes to the unlabeled set of images. The presence
of out-of-domain data (novel classes), high class-imbalance, and fine-grained
similarity between classes poses significant challenges for existing
semi-supervised recognition techniques in the literature. The dataset is
available here: \url{https://github.com/cvl-umass/semi-inat-2020}


Abstract:
  Research managers benchmarking universities against international peers face
the problem of affiliation disambiguation. Different databases have taken
separate approaches to this problem and discrepancies exist between them.
Bibliometric data sources typically conduct a disambiguation process that
unifies variant institutional names and those of its sub-units so that
researchers can then search all records from that institution using a single
unified name. This study examined affiliation discrepancies between Scopus, Web
of Science, Dimensions, and Microsoft Academic for 18 Arab universities over a
five-year period. We confirmed that digital object identifiers (DOIs) are
suitable for extracting comparable scholarly material across databases and
quantified the affiliation discrepancies between them. A substantial share of
records assigned to the selected universities in any one database were not
assigned to the same university in another. The share of discrepancy was higher
in the larger databases, Dimensions and Microsoft Academic. The smaller, more
selective databases, Scopus and especially Web of Science tended to agree to a
greater degree with affiliations in the other databases. Manual examination of
affiliation discrepancies showed they were caused by a mixture of missing
affiliations, unification differences, and assignation of records to the wrong
institution.


Abstract:
  In this manuscript we investigate the analouge of the Brill-Noether problem
for smooth curves in the case of normal surface singularities. We determine the
maximal possible value of $h^1$ of line bundles without fixed components in the
Picard group $\pic^{l'}(\tX)$ in the following cases: for some special Chern
classes $l'$ if $\tX$ is a resolution of a splice quotient singularity $(X, 0)$
and for arbitrary Chern classes in the case of weighted homogenous
singularities. Motivated by this problem, we define the \emph{virtual
cohomology numbers} $h^1_{virt}(l')$ for all Chern classes $l'$ such that
$h^1_{virt}(0)$ is the canonical normalized Seiberg-Witten invariant and we
generalize the duality formulae of Seiberg-Witten invariants obtained by the
authors and A. N\'emethi in \cite{LNNdual}, for the virtual cohomology numbers.


Abstract:
  We present new deep, high-resolution, 1.5 GHz observations of the
prototypical nearby Perseus galaxy cluster from the Karl G. Jansky Very Large
Array. We isolate for the first time the complete tail of radio emission of the
bent-jet radio galaxy NGC 1272, which had been previously mistaken to be part
of the radio mini-halo. The possibility that diffuse radio galaxy emission
contributes to mini-halo emission may be a general phenomenon in relaxed
cool-core clusters, and should be explored. The collimated jets of NGC 1272
initially bend to the west, and then transition eastward into faint, 60
kpc-long extensions with eddy-like structures and filaments. We suggest
interpretations for these structures that involve bulk motions of intracluster
gas, the galaxy's orbit in the cluster including projection effects, and the
passage of the galaxy through a sloshing cold front. Instabilities and
turbulence created at the surface of this cold front and in the turbulent wake
of the infalling host galaxy most likely play a role in the formation of the
observed structures. We also discover a series of faint rings, south-east of
NGC 1272, which are a type of structure that has never been seen before in
galaxy clusters.


Abstract:
  Recent years have witnessed the fast development of the emerging topic of
Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph
learning approaches to model users' preferences and intentions as well as
items' characteristics for recommendations. Differently from other RS
approaches, including content-based filtering and collaborative filtering, GLRS
are built on graphs where the important objects, e.g., users, items, and
attributes, are either explicitly or implicitly connected. With the rapid
development of graph learning techniques, exploring and exploiting homogeneous
or heterogeneous relations in graphs are a promising direction for building
more effective RS. In this paper, we provide a systematic review of GLRS, by
discussing how they extract important knowledge from graph-based
representations to improve the accuracy, reliability and explainability of the
recommendations. First, we characterize and formalize GLRS, and then summarize
and categorize the key challenges and main progress in this novel research
area. Finally, we share some new research directions in this vibrant area.