Abstract:
  In this work, the possibility of using niobium oxide recovered from tailings
from the Penouta Sn-Nb-Ta deposit (located at the North of Spain) as starting
material for growing microstructures is demonstrated. The properties of the
starting material have been studied to understand its crystal structure,
quality and purity. Recovered niobium oxide powders are mainly of TT-Nb2O5.
These powders have been used to grow Nb22O54 microrods by an evaporation method
in an argon atmosphere. Different characterization techniques (X-Ray
diffraction, scanning electron microscopy, micro-Raman spectroscopy,
luminescence) have been used to determine the properties of Nb22O54 microrods,
mainly focusing on the crystal quality and refractive index. The present study
opens the way to the transformation of waste (mine tailings) into a material of
high technological value as niobium oxide, and its reintroduction into the
value chain for a wide range of applications, from coatings to batteries and
supercapacitors.


Abstract:
  In the modern city, the utilization rate of public transportation attached
importance to the efficiency of public traffic. However, the unreasonable
distribution of transportation platforms results in a low utilization rate. In
this paper, we researched and evaluated the distribution of platforms -- bus
and subway -- and proposed a method, called "partial area cluster" (PAC), to
improve the utilization by changing and renewing the original distribution. The
novel method was based on the K-means algorithm in the field of machine
learning. PAC worked to search the suitable bus platforms as the center and
modified the original one to the subway. Experience has shown that the use of
public transport resources has increased by 20%. The study uses a similar
cluster algorithm to solve transport networks' problems in a novel but
practical term. As a result, the PAC is expected to be used extensively in the
transportation system construction process.


Abstract:
  A growing number of commercial satellite companies provide easily accessible
satellite imagery. Overhead imagery is used by numerous industries including
agriculture, forestry, natural disaster analysis, and meteorology. Satellite
images, just as any other images, can be tampered with image manipulation
tools. Manipulation detection methods created for images captured by "consumer
cameras" tend to fail when used on satellite images due to the differences in
image sensors, image acquisition, and processing. In this paper we propose an
unsupervised technique that uses a Vision Transformer to detect spliced areas
within satellite images. We introduce a new dataset which includes manipulated
satellite images that contain spliced objects. We show that our proposed
approach performs better than existing unsupervised splicing detection
techniques.


Abstract:
  A new era of exploration of the low radio frequency Universe from the Moon
will soon be underway with landed payload missions facilitated by NASA's
Commercial Lunar Payload Services (CLPS) program. CLPS landers are scheduled to
deliver two radio science experiments, ROLSES to the nearside and LuSEE to the
farside, beginning in 2021. These instruments would be pathfinders for a 10-km
diameter interferometric array, FARSIDE, composed of 128 pairs of dipole
antennas proposed to be delivered to the lunar surface later in the decade.
ROLSES and LuSEE, operating at frequencies from 100 kHz to a few tens of MHz,
will investigate the plasma environment above the lunar surface and measure the
fidelity of radio spectra on the surface. Both use electrically-short,
spiral-tube deployable antennas and radio spectrometers based upon previous
flight models. ROLSES will measure the photoelectron sheath density to better
understand the charging of the lunar surface via photoionization and impacts
from the solar wind, charged dust, and current anthropogenic radio frequency
interference. LuSEE will measure the local magnetic field and exo-ionospheric
density, interplanetary radio bursts, Jovian and terrestrial natural radio
emission, and the galactic synchrotron spectrum. FARSIDE, and its precursor
risk-reduction six antenna-node array PRIME, would be the first radio
interferometers on the Moon. FARSIDE would break new ground by imaging radio
emission from Coronal Mass Ejections (CME) beyond 2 solar radii, monitor
auroral radiation from the B-fields of Uranus and Neptune (not observed since
Voyager), and detect radio emission from stellar CMEs and the magnetic fields
of nearby potentially habitable exoplanets.


Abstract:
  The motive associated to the second Symanzik polynomial of the double-box
two-loop Feynman graph with generic masses and momenta is shown to be an
elliptic curve.


Abstract:
  The diagnosis of superficial fungal infections in dermatology is still mostly
based on manual direct microscopic examination with Potassium Hydroxide (KOH)
solution. However, this method can be time consuming and its diagnostic
accuracy rates vary widely depending on the clinician's experience. With the
increase of neural network applications in the field of clinical microscopy, it
is now possible to automate such manual processes increasing both efficiency
and accuracy. This study presents a deep neural network structure that enables
the rapid solutions for these problems and can perform automatic fungi
detection in grayscale images without dyes. 160 microscopic field photographs
containing the fungal element, obtained from patients with onychomycosis, and
297 microscopic field photographs containing dissolved keratin obtained from
normal nails were collected. Smaller patches containing 4234 fungi and 4981
keratin were extracted from these images. In order to detect fungus and
keratin, VGG16 and InceptionV3 models were developed. The VGG16 model had
95.98% accuracy, and the area under the curve (AUC) value of 0.9930, while the
InceptionV3 model had 95.90% accuracy and the AUC value of 0.9917. However,
average accuracy and AUC value of clinicians is 72.8% and 0.87, respectively.
This deep learning model allows the development of an automated system that can
detect fungi within microscopic images.


Abstract:
  System diagnosis is process of identifying faulty nodes in a system. An
efficient diagnosis is crucial for a multiprocessor system. The BGM diagnosis
model is a modification of the PMC diagnosis model, which is a test-based
diagnosis. In this paper, we present a specific structure and propose an
algorithm for diagnosing a node in a system under the BGM model. We also give a
polynomial-time algorithm that a node in a hypercube-like network can be
diagnosed correctly in three test rounds under the BGM diagnosis model.


Abstract:
  To realise a planned high-luminosity and high-energy $e^+e^-$-collider, as
the ILC, a large amount of positrons have to be produced and the accelerated
particles have to be captured and matched according to the damping ring
acceptances. %There exist several technical possibilities. In this contribution
a new promising alternative method for capturing positrons will be presented,
the application of the plasma lens as an optical matching device. It will be
compared with the current matching device proposed for the ILC, namely the
quarter wave transformer. An advantage of the plasma lens is the different
magnetic field component, which focuses the divergent beam in a more effective
manner. Therefore it will be shown in this paper that the yield requirements
could be achieved more easily. The plasma lens can actually be a promising
alternative for focusing beams as soon as the technical feasibility has been
approved.\\ In the simulation, a tapered active plasma lens has been optimized
using the approximation of a homogeneous electric current density constant in
time. The optimization process led to a plasma lens design that improves on the
ILC's currently proposed optical matching device, namely the quarter wave
transformer, by approximately $50-100\%$. Furthermore the design has been shown
to guarantee a stable captured positron yield within $\pm1.5\%$ for single,
independent parameter deviations of about $\pm10\%$.


Abstract:
  The present work reports on LES of turbulent reacting flow of the
Delft-Jet-in-Hot-Coflow (DJHC) burner, emulating MILD combustion, with
transported PDF based combustion models using ANSYS FLUENT 13.0. Two different
eddy viscosity models for LES (Dynamic Smagorinsky and Kinetic Energy
Transport) along with two solution approaches for PDF transport equation, i.e.
Eulerian and Lagrangian, have been used in the present study. Moreover, the
effects of chemical kinetics and the micro-mixing models have also been
investigated for two different fuel jet Reynolds number (Re = 4,100 and Re =
8,800). The mean velocity and turbulent kinetic energy predicted by the
different models are in good agreement with experimental data. Both the
composition PDF models predict an early ignition resulting in higher radial
mean temperature predictions at burner exit. The models, however, correctly
predict the formation mechanism of ignition kernels and the decreasing trend of
the lift-off height with increasing jet Reynolds number, as observed
experimentally.


Abstract:
  A valley-contrasting Berry curvature in bilayer transition metal
dichalcogenides with spin-orbit coupling can generate valley magnetization when
the inversion symmetry is broken, for example, by an electric field, regardless
of time-reversal symmetry. A nontrivial Berry curvature can also lead to
anomalous transport responses, such as the anomalous Hall effect and the
anomalous Nernst effect. Applied to a bilayer WSe$_2$, an electric field can
tune the Berry curvature and orbital magnetic moment, which has important
consequences for the orbital magnetization and the anomalous Nernst responses.
The orbital magnetization and its two contributions, one due to the magnetic
moment and one due to the Berry curvature, are calculated and interpreted in
terms of opposite circulating currents of the bands in the two layers. The
valley anomalous Nernst coefficient and spin Nernst coefficient are also
calculated. We find that a finite electric field leads to peaks and dips in the
Nernst responses that have the signs of the Berry curvatures of the bands and
are proportional to their magnitudes; it also enhances the valley Nernst
responses. These experimentally verifiable findings may be promising for
caloritronic applications.


Abstract:
  We present in this work the calibration procedure and a performance study of
long scintillator bars used for the time-of-flight (TOF) measurement in the
HADES experiment. The digital front-end electronics installed at the TOF
detector required to develop novel calibration methods. The exceptional
performance of the spectrometer for particle identification and pointing
accuracy allows one to determine in great detail the response of scintillators
to minimum ionizing particles. A substantial position sensitivity of the
calibration parameters has been found, in particular for the signal time walk.
After including the position dependence, the timing accuracy for minimum
ionizing particles was improved from 190~ps to 135~ps for the shortest rods
(1475 mm) and to 165~ps for the longest (2356 mm). These results are in
accordance with the time degradation length of the scintillator bars, as
determined from previous measurements.


Abstract:
  Sequential recommender systems aim to model users' evolving interests from
their historical behaviors, and hence make customized time-relevant
recommendations. Compared with traditional models, deep learning approaches
such as CNN and RNN have achieved remarkable advancements in recommendation
tasks. Recently, the BERT framework also emerges as a promising method,
benefited from its self-attention mechanism in processing sequential data.
However, one limitation of the original BERT framework is that it only
considers one input source of the natural language tokens. It is still an open
question to leverage various types of information under the BERT framework.
Nonetheless, it is intuitively appealing to utilize other side information,
such as item category or tag, for more comprehensive depictions and better
recommendations. In our pilot experiments, we found naive approaches, which
directly fuse types of side information into the item embeddings, usually bring
very little or even negative effects. Therefore, in this paper, we propose the
NOninVasive self-attention mechanism (NOVA) to leverage side information
effectively under the BERT framework. NOVA makes use of side information to
generate better attention distribution, rather than directly altering the item
embedding, which may cause information overwhelming. We validate the NOVA-BERT
model on both public and commercial datasets, and our method can stably
outperform the state-of-the-art models with negligible computational overheads.


Abstract:
  One strategy for arresting propagating detonation waves in pipes is by
imposing a sudden area enlargement, which provides a rapid lateral divergence
of the gases in the reaction zone and attenuates the leading shock. For
sufficiently small tube diameter, the detonation decays to a deflagration and
the shock decays to negligible strengths. This is known as the critical tube
diameter problem. In the present study, we provide a closed form model to
predict the detonation quenching for 2D channels. Whitham's geometric shock
dynamics, coupled with a shock evolution law based on shocks sustained by a
constant source obtained by the shock change equations of Radulescu, is shown
to capture the lateral shock dynamics response to the failure wave originating
at the expansion corner. A criterion for successful detonation transmission to
open space is that the lateral strain rate provided by the failure wave not
exceed the critical strain rate of steady curved detonations. Using the
critical lateral strain rate obtained by He and Clavin, a closed form solution
is obtained for the critical channel opening permitting detonation
transmission. The predicted critical channel width is found in very good
agreement with our recent experiments and simulations of diffracting
H$_2$/O$_2$/Ar detonations.


Abstract:
  We present the two-point cross-correlation function between high-mass X-ray
binaries (HMXBs) in the Small Magellanic Cloud (SMC) and their likely
birthplaces (OB Associations: OBAs). This function compares the spatial
correlation between the observed HMXB and OBA populations against mock catalogs
in which the members are distributed randomly across the sky. A significant
correlation (15 sigma) is found for the HMXB and OBA populations when compared
with a randomized catalog in which the OBAs are distributed uniformly over the
SMC. A less significant correlation (4 sigma) is found for a randomized catalog
of OBAs built with a bootstrap method. However, no significant correlation is
detected when the randomized catalogs assume the form of a Gaussian ellipsoid
or a distribution that reflects the star-formation history from 40 Myr ago.
Based on their observed distributions and assuming a range of migration
timescales, we infer that the average value of the kick velocity inherited by
an HMXB during the formation of its compact object is 2-34 km/s. This is
considerably less than the value obtained for their counterparts in the Milky
Way hinting that the galactic environment affecting stellar evolution plays a
role in setting the average kick velocity of HMXBs.


Abstract:
  Knowledge-intensive tasks such as question answering often require
assimilating information from different sections of large inputs such as books
or article collections. We propose ReadTwice, a simple and effective technique
that combines several strengths of prior approaches to model long-range
dependencies with Transformers. The main idea is to read text in small
segments, in parallel, summarizing each segment into a memory table to be used
in a second read of the text. We show that the method outperforms models of
comparable size on several question answering (QA) datasets and sets a new
state of the art on the challenging NarrativeQA task, with questions about
entire books. Source code and pre-trained checkpoints for ReadTwice can be
found at https://goo.gle/research-readtwice.


Abstract:
  The monogamy relations of entanglement are highly significant. However, they
involve only amounts of entanglement shared by different subsystems. Results on
monogamy relations between entanglement and other kinds of correlations, and
particularly classical correlations, are very scarce. Here we experimentally
observe a tradeoff relation between internal quantum nonseparability and
external total correlations in a photonic system and found that even purely
classical external correlations have a detrimental effect on internal
nonseparability. The nonseparability we consider, measured by the concurrence,
is between different degrees of freedom within the same photon, and the
external classical correlations, measured by the standard quantum mutual
information, are generated between the photons of a photon pair using the
time-bin method. Our observations show that to preserve the internal
entanglement in a system, it is necessary to maintain low external
correlations, including classical ones, between the system and its environment.


Abstract:
  A very intense gamma beam of the Gamma Factory facility proposed at CERN can
be used to generate radioactive ion beams (RIBs) with high production yields
and study the structure of exotic neutron-rich nuclei. The radioactive nuclides
are generated via photo-fission in several actinide targets and thermalized in
high-purity cryogenic helium, filling a gas cell which is enclosing the
targets. Electric fields are used to extract heavy ions and form RIBs which can
be sent to various selection and measurement stations. Estimates for the
production and extraction yields of exotic neutron-rich nuclei with such a
setup are provided. A study of the impact of space charge, build-up inside the
gas cell, on the extraction properties is presented and it is demonstrated that
the beam needs to be chopped for achieving optimal extraction yields.


Abstract:
  In this note we study strong contractivity of monotone systems and
equilibrium-contractivity of positive systems with respect to non-Euclidean
norms. We first introduce the notion of conic matrix measure and study its
properties. Using conic matrix measures and weak pairings, we characterize
strongly contracting monotone systems with respect to non-Euclidean norms. This
framework leads to novel results on (i) the stability of monotone separable
systems, (ii) the strong contractivity of excitatory Hopfield neural networks,
and (iii) a strong version of the Matrosov-Bellman comparison lemma. We also
characterize equilibrium-contracting positive systems with respect to
non-Euclidean norms and provide a sufficient condition for
equilibrium-contractivity using conic matrix measures. This framework leads to
novel results on (i) the equilibrium-contractivity of positive separable
systems, and (ii) a comparison-based framework for interconnected systems.


Abstract:
  Transformer-based models have achieved state-of-the-art results in a wide
range of natural language processing (NLP) tasks including document
summarization. Typically these systems are trained by fine-tuning a large
pre-trained model to the target task. One issue with these transformer-based
models is that they do not scale well in terms of memory and compute
requirements as the input length grows. Thus, for long document summarization,
it can be challenging to train or fine-tune these models. In this work, we
exploit large pre-trained transformer-based models and address long-span
dependencies in abstractive summarization using two methods: local
self-attention; and explicit content selection. These approaches are compared
on a range of network configurations. Experiments are carried out on standard
long-span summarization tasks, including Spotify Podcast, arXiv, and PubMed
datasets. We demonstrate that by combining these methods, we can achieve
state-of-the-art results on all three tasks in the ROUGE scores. Moreover,
without a large-scale GPU card, our approach can achieve comparable or better
results than existing approaches.


Abstract:
  The specific heat is a central property of condensed matter systems including
polymers and oligomers in their condensed phases. Yet, predictions of this
quantity from molecular simulations and successful comparisons to experimental
data are scarce if existing at all. One reason for this may be that the
internal energy and thus the specific heat cannot be coarse-grained so that
they defy their rigorous computation with united-atom models. Moreover, many
modes in a polymer barely contribute to the specific heat because of their
quantum mechanical nature. Here, we demonstrate that an analysis of the
mass-weighted velocity autocorrelation function allows specific heat
predictions to be corrected for quantum effects so that agreement with
experimental data is on par with predictions of other routinely computed
quantities. We outline how to construct corrections for both all-atom and
united-atom descriptions of chain molecules. Corrections computed for eleven
hydrocarbon oligomers and commodity polymers deviate by less than
$k_\textrm{B}/10$ within a subset of nine molecules. Our results may benefit
the prediction of heat conductivity.


Abstract:
  In an incomplete market underpinned by the trinomial model, we consider two
investors : an ordinary agent whose decisions are driven by public information
and an insider who possesses from the beginning a surplus of information
encoded through a random variable for which he or she knows the outcome.
Through the definition of an auxiliary model based on a marked binomial
process, we handle the trinomial model as a volatility one, and use the
stochastic analysis and Malliavin calculus toolboxes available in that context.
In particular, we connect the information drift, the drift to eliminate in
order to preserve the martingale property within an initial enlargement of
filtration in terms of the Malliavin derivative. We solve explicitly the agent
and the insider expected logarithmic utility maximisation problems and provide
a hedging formula for replicable claims. We identify the insider expected
additional utility with the Shannon entropy of the extra information, and
examine then the existence of arbitrage opportunities for the insider.


Abstract:
  Algorithmic risk assessments are used to inform decisions in a wide variety
of high-stakes settings. Often multiple predictive models deliver similar
overall performance but differ markedly in their predictions for individual
cases, an empirical phenomenon known as the "Rashomon Effect." These models may
have different properties over various groups, and therefore have different
predictive fairness properties. We develop a framework for characterizing
predictive fairness properties over the set of models that deliver similar
overall performance, or "the set of good models." Our framework addresses the
empirically relevant challenge of selectively labelled data in the setting
where the selection decision and outcome are unconfounded given the observed
data features. Our framework can be used to 1) replace an existing model with
one that has better fairness properties; or 2) audit for predictive bias. We
illustrate these uses cases on a real-world credit-scoring task and a
recidivism prediction task.


Abstract:
  Neural networks have achieved remarkable performance in computer vision,
however they are vulnerable to adversarial examples. Adversarial examples are
inputs that have been carefully perturbed to fool classifier networks, while
appearing unchanged to humans. Based on prior works on detecting adversaries,
we propose a structured methodology of augmenting a deep neural network (DNN)
with a detector subnetwork. We use $\textit{Adversarial Noise Sensitivity}$
(ANS), a novel metric for measuring the adversarial gradient contribution of
different intermediate layers of a network. Based on the ANS value, we append a
detector to the most sensitive layer. In prior works, more complex detectors
were added to a DNN, increasing the inference computational cost of the model.
In contrast, our structured and strategic addition of a detector to a DNN
reduces the complexity of the model while making the overall network
adversarially resilient. Through comprehensive white-box and black-box
experiments on MNIST, CIFAR-10, and CIFAR-100, we show that our method improves
state-of-the-art detector robustness against adversarial examples. Furthermore,
we validate the energy efficiency of our proposed adversarial detection
methodology through an extensive energy analysis on various hardware scalable
CMOS accelerator platforms. We also demonstrate the effects of quantization on
our detector-appended networks.


Abstract:
  In this work, we give a proof of the globally sharp asymptotic profiles for
the spin-$\mathfrak{s}$ fields on a Schwarzschild background, including the
scalar field $(\mathfrak{s}=0)$, the Maxwell field $(\mathfrak{s}=\pm 1)$ and
the linearized gravity $(\mathfrak{s}=\pm 2)$. The conjectured Price's law in
the physics literature which predicts the sharp estimates of the spin $s=\pm
\mathfrak{s}$ components towards the future null infinity as well as in a
compact region is shown. Further, we confirm the heuristic claim by Barack and
Ori that the spin $+1, +2$ components have an extra power of decay at the event
horizon than the conjectured Price's law. The asymptotics are derived via a
unified, detailed analysis of the Teukolsky master equation that is satisfied
by all these components.


Abstract:
  With every locally compact group $G$, one can associate several interesting
bi-invariant subspaces $X(G)$ of the weakly almost periodic functions
$\mathrm{WAP}(G)$ on $G$, each of which captures parts of the representation
theory of $G$. Under certain natural assumptions, such a space $X(G)$ carries a
unique invariant mean and has a natural predual, and we view the
weak$^*$-continuity of this mean as a rigidity property of $G$. Important
examples of such spaces $X(G)$, which we study explicitly, are the algebra
$M_{\mathrm{cb}}A_p(G)$ of $p$-completely bounded multipliers of the
Fig\`a-Talamanca-Herz algebra $A_p(G)$ and the $p$-Fourier-Stieltjes algebra
$B_p(G)$. In the setting of connected Lie groups $G$, we relate the
weak$^*$-continuity of the mean on these spaces to structural properties of
$G$. Our results generalise results of Bekka, Kaniuth, Lau and Schlichting.


Abstract:
  We prove an exponential separation for the sample complexity between the
standard PAC-learning model and a version of the Equivalence-Query-learning
model. We then show that this separation has interesting implications for
adversarial robustness. We explore a vision of designing an adaptive defense
that in the presence of an attacker computes a model that is provably robust.
In particular, we show how to realize this vision in a simplified setting.
  In order to do so, we introduce a notion of a strong adversary: he is not
limited by the type of perturbations he can apply but when presented with a
classifier can repetitively generate different adversarial examples. We explain
why this notion is interesting to study and use it to prove the following.
There exists an efficient adversarial-learning-like scheme such that for every
strong adversary $\mathbf{A}$ it outputs a classifier that (a) cannot be
strongly attacked by $\mathbf{A}$, or (b) has error at most $\epsilon$. In both
cases our scheme uses exponentially (in $\epsilon$) fewer samples than what the
PAC bound requires.


Abstract:
  In this short paper an idea is sketched, how to support drivers of an
autonomous vehicle in taking back control of the vehicle after a longer section
of autonomous cruising. The hypothesis is that a clear communication about the
location and behavior of relevant objects in the environment will help the
driver to quickly grasp the situational context and thus support drivers in
safely handling the ongoing driving situation manually after take-over. Based
on this hypothesis, a research concept is sketched, which entails the necessary
components as well as the disciplines involved.


Abstract:
  Inspired by Fr\"{o}hlich-Spencer and subsequent authors who introduced the
notion of contour for long-range systems, we provide a definition of contour
and a direct proof for the phase transition for ferromagnetic long-range Ising
models on $\mathbb{Z}^d$, $d\geq 2$. The argument, which is based on a
multi-scale analysis, works for the sharp region $\alpha>d$ and improves
previous results obtained by Park for $\alpha>3d+1$, and by Ginibre, Grossmann,
and Ruelle for $\alpha> d+1$, where $\alpha$ is the power of the coupling
constant. The key idea is to avoid a large number of small contours. As an
application, we prove the persistence of the phase transition when we add a
polynomial decaying magnetic field with power $\delta>0$ as $h^*|x|^{-\delta}$,
where $h^* >0$. For $d<\alpha<d+1$, the phase transition occurs when
$\delta>d-\alpha$, and when $h^*$ is small enough over the critical line
$\delta=d-\alpha$. For $\alpha \geq d+1$, $\delta>1$ it is enough to prove the
phase transition, and for $\delta=1$ we have to ask $h^*$ small. The natural
conjecture is that this region is also sharp for the phase transition problem
when we have a decaying field.


Abstract:
  The field of e-learning has emerged as a topic of interest in academia due to
the increased ease of accessing the Internet using using smart-phones and
wireless devices. One of the challenges facing e-learning platforms is how to
keep students motivated and engaged. Moreover, it is also crucial to identify
the students that might need help in order to make sure their academic
performance doesn't suffer. To that end, this paper tries to investigate the
relationship between student engagement and their academic performance. Apriori
association rules algorithm is used to derive a set of rules that relate
student engagement to academic performance. Experimental results' analysis done
using confidence and lift metrics show that a positive correlation exists
between students' engagement level and their academic performance in a blended
e-learning environment. In particular, it is shown that higher engagement often
leads to better academic performance. This cements the previous work that
linked engagement and academic performance in traditional classrooms.


Abstract:
  As software systems become more complex, modern software development requires
more attention to human perspectives, and active participation of development
teams in requirements elicitation tasks. In this context, incomplete or
ambiguous requirements descriptions do not guide the development of good
software products. We hypothesize that the text auto-completion feature
improves the quality of the software requirements artifacts. We present the
motivation for this study, related works, our approach and future research
efforts.


Abstract:
  We study mass preserving transport of passive tracers in the low-diffusivity
limit using Lagrangian coordinates. Over finite-time intervals, the
solution-operator of the nonautonomous diffusion equation is approximated by
that of a time-averaged diffusion equation. We show that leading order
asymptotics that hold for functions [Krol, 1991] extend to the dominant
nontrivial singular value. This answers questions raised in [Karrasch & Keller,
2020]. The generator of the time-averaged diffusion/heat semigroup is a Laplace
operator associated to a weighted manifold structure on the material manifold.
We show how geometrical properties of this weighted manifold directly lead to
physical transport quantities of the nonautonomous equation in the
low-diffusivity limit.


Abstract:
  Creating scalable, high performance PDE-based simulations requires a suitable
combination of discretizations, differential operators, preconditioners and
solvers. The required combination changes with the application and with the
available hardware, yet software development time is a severely limited
resource for most scientists and engineers. Here we demonstrate that generating
simulation code from a high-level Python interface provides an effective
mechanism for creating high performance simulations from very few lines of user
code. We demonstrate that moving from one supercomputer to another can require
significant algorithmic changes to achieve scalable performance, but that the
code generation approach enables these algorithmic changes to be achieved with
minimal development effort.


Abstract:
  Multispecies swarms are found for microorganisms living in microfluidic
environments where they can take advantage of collective motions during
transport and spreading. Nevertheless, there is a general lack of physical
understandings of the origins of the multiscale unstable dynamics. Here we
build a computation model to study the binary suspensions of rear- and
front-actuated microswimmers, or respectively the so-called "pusher" and
"puller" particles, that have different populations and swimming speeds. We
perform direct particle simulations to reveal that even in the scenarios of
"stress-balanced" mixtures which produce approximately zero net extra stresses,
the longtime dynamics can exhibit non-trivial density fluctuations and
spatially-correlated motions. We then construct a continuum kinetic model and
perform linear stability analysis to reveal the underlying mechanisms of
hydrodynamic instabilities. Our theoretical predictions qualitatively agree
with numerical results and explain the onsets of the observed collective
motions.


Abstract:
  We conducted a set of Rapid Reviews to characterize Internet of Things
facets. We formatted a generic meta-protocol that was instantiated for each of
the six facets presented (Connectivity, Things, Behavior, Smartness,
Interactivity, and Environment)and considering the issue of Security, one of
the most important and frequent challenges in the context of IoT. The
meta-protocol is detailed and the results of each review are presented.


Abstract:
  We revisit the arguments underlying two well-known arrival-time distributions
in quantum mechanics, viz., the Aharonov-Bohm and Kijowski (ABK) distribution,
applicable for freely moving particles, and the quantum flux (QF) distribution.
An inconsistency in the original axiomatic derivation of Kijowski's result is
pointed out, along with an inescapable consequence of the "negative arrival
times" inherent to this proposal (and generalizations thereof). The ABK
free-particle restriction is lifted in a discussion of an explicit arrival-time
setup featuring a charged particle moving in a constant magnetic field. A
natural generalization of the ABK distribution is in this case shown to be
critically gauge-dependent. A direct comparison to the QF distribution, which
does not exhibit this flaw, is drawn (its acknowledged drawback concerning the
quantum backflow effect notwithstanding).


Abstract:
  In this paper, we propose a matching theory based multi-user cooperative
computing (MUCC) scheme to minimize the overall energy consumption of a group
of user equipments (UEs), where the UEs can be classified into the following
roles: resource demander (RD), resource provider (RP), and standalone UE (SU).
We first determine the role of each UE by leveraging the roommate matching
method. Then, we propose the college admission based algorithm to divide the
UEs into multiple cooperation groups, each consisting of one RP and multiple
RDs. Next, we propose the rotation swap operation to further improve the
performance without deteriorating the system stability. Finally, we present an
effective task offloading algorithm to minimize the energy consumption of all
the cooperation groups. The simulation results verify the effectiveness of the
proposed scheme.


Abstract:
  Motivated by problems of learning to rank long item sequences, we introduce a
variant of the cascading bandit model that considers flexible length sequences
with varying rewards and losses. We formulate two generative models for this
problem within the generalized linear setting, and design and analyze upper
confidence algorithms for it. Our analysis delivers tight regret bounds which,
when specialized to vanilla cascading bandits, results in sharper guarantees
than previously available in the literature. We evaluate our algorithms on a
number of real-world datasets, and show significantly improved empirical
performance as compared to known cascading bandit baselines.


Abstract:
  In this paper the finite-time stabilization problem is solved for a linear
time-varying system with unknown control direction by exploiting a modified
version of the classical extremum seeking algorithm. We propose to use a
suitable oscillatory input to modify the system dynamics, at least in an
average sense, so as to satisfy a Differential Linear Matrix Inequality (DLMI)
condition which in turns guarantees that the system's state remains inside a
prescribed time varying hyper-ellipsoid in the state space. The finite-time
stability (FTS) of the averaged dynamics implies the FTS of the original
system, as the distance between the original and the averaged dynamics can be
made arbitrarily small by choosing a sufficiently high value of the dithering
frequency used by the extremum seeking algorithm. An estimate of the necessary
minimum dithering/mixing frequency is provided, and the effectiveness of the
proposed finite-time stabilization approach is analysed by means of numerical
examples.


Abstract:
  Mechanical modelling of poroelastic media under finite strain is usually
carried out via phenomenological models neglecting complex micro-macro scales
interdependency. One reason is that the mathematical two-scale analysis is only
straightforward assuming infinitesimal strain theory. Exploiting the potential
of ANNs for fast and reliable upscaling and localisation procedures, we propose
an incremental numerical approach that considers rearrangement of the cell
properties based on its current deformation, which leads to the remodelling of
the macroscopic model after each time increment. This computational framework
is valid for finite strain and large deformation problems while it ensures
infinitesimal strain increments within time steps. The full effects of the
interdependency between the properties and response of macro and micro scales
are considered for the first time providing more accurate predictive analysis
of fluid-saturated porous media which is studied via a numerical consolidation
example. Furthermore, the (nonlinear) deviation from Darcy's law is captured in
fluid filtration numerical analyses. Finally, the brain tissue mechanical
response under uniaxial cyclic test is simulated and studied.


Abstract:
  Choi and Shin (2020) have constructed a bootstrap-based test for
change-points in panels with temporal and and/or cross-sectional dependence.
They have compared their test to several other proposed tests. We demonstrate
that by an appropriate, data-adaptive choice of the block length, the
change-point test by Sharipov, Tewes, Wendler (2016) can at least cope with
mild temporal dependence, the size distortion of this test is not as severe as
claimed by Choi and Shin (2020).


Abstract:
  In this article, we interpolate a given real analytic spacelike curve $a$ in
Lorentz-Minkowski space $\mathbb{L}^3$ to another real analytic spacelike curve
$c$, which is "close" enough to $a$ in a certain sense, by a maximal surface
using inverse function theorem for Banach spaces. Using the same method we also
interpolate a given real analytic curve $a$ in Euclidean space $\mathbb{E}^3$
to another real analytic curve $c$, which is "close" enough to $a$ in a certain
sense, by a minimal surface. The Bj\"orling problem and Schwartz's solution to
it play an important role.


Abstract:
  Decomposable tasks are complex and comprise of a hierarchy of sub-tasks.
Spoken intent prediction, for example, combines automatic speech recognition
and natural language understanding. Existing benchmarks, however, typically
hold out examples for only the surface-level sub-task. As a result, models with
similar performance on these benchmarks may have unobserved performance
differences on the other sub-tasks. To allow insightful comparisons between
competitive end-to-end architectures, we propose a framework to construct
robust test sets using coordinate ascent over sub-task specific utility
functions. Given a dataset for a decomposable task, our method optimally
creates a test set for each sub-task to individually assess sub-components of
the end-to-end model. Using spoken language understanding as a case study, we
generate new splits for the Fluent Speech Commands and Snips SmartLights
datasets. Each split has two test sets: one with held-out utterances assessing
natural language understanding abilities, and one with held-out speakers to
test speech processing skills. Our splits identify performance gaps up to 10%
between end-to-end systems that were within 1% of each other on the original
test sets. These performance gaps allow more realistic and actionable
comparisons between different architectures, driving future model development.
We release our splits and tools for the community.


Abstract:
  We propose BaySize, a sample size calculator for phase I clinical trials
using Bayesian models. BaySize applies the concept of effect size in dose
finding, assuming the MTD is defined based on an equivalence interval.
Leveraging a decision framework that involves composite hypotheses, BaySize
utilizes two prior distributions, the fitting prior (for model fitting) and
sampling prior (for data generation), to conduct sample size calculation under
desirable statistical power. Look-up tables are generated to facilitate
practical applications. To our knowledge, BaySize is the first sample size tool
that can be applied to a broad range of phase I trial designs.


Abstract:
  In their early stages, protoplanetary discs are sufficiently massive to
undergo gravitational instability (GI). This instability is thought to be
involved in mass accretion, planet formation via gas fragmentation, the
generation of spiral density waves, and outbursts. A key and very recent area
of research is the interaction between the GI and magnetic fields in young
protoplanetary discs, in particular whether this instability is able to sustain
a magnetic field via a dynamo. We conduct three-dimensional, stratified
shearing-box simulations using two independent codes, PLUTO and Athena++, to
characterise the GI dynamo in poorly ionised protostellar discs subject to
ambipolar diffusion. We find that the dynamo operates across a large range of
ambipolar Elssaser number Am (which characterises the strength of ambipolar
diffusion) and is particularly strong in the regime Am=10-100, with typical
magnetic to thermal energy ratios of order unity. The dynamo is only weakly
dependent on resolution (at least for Am <100), box size, and cooling law. The
magnetic field is produced by the combination of differential rotation and
large-scale vertical roll motions associated with spiral density waves. Our
results have direct implications for the dynamo process in young protoplanetary
discs and possibly some regions of AGN discs.


Abstract:
  In this paper, we take a critical and fresh look at the downlink
multi-antenna NOMA literature. Instead of contrasting NOMA with OMA, we
contrast NOMA with two other baselines. The first is conventional Multi-User
Linear Precoding (MULP). The second is Rate-Splitting Multiple Access (RSMA)
based on multi-antenna Rate-Splitting (RS) and SIC. We show that there is some
confusion about the benefits of NOMA, and we dispel the associated
misconceptions. First, we highlight why NOMA is inefficient in multi-antenna
settings based on basic multiplexing gain analysis. We stress that the issue
lies in how the NOMA literature has been hastily applied to multi-antenna
setups, resulting in a misuse of spatial dimensions and therefore loss in
multiplexing gains and rate. Second, we show that NOMA incurs a severe
multiplexing gain loss despite an increased receiver complexity due to an
inefficient use of SIC receivers. Third, we emphasize that much of the merits
of NOMA are due to the constant comparison to OMA instead of comparing it to
MULP and RS baselines. We then expose the pivotal design constraint that
multi-antenna NOMA requires one user to fully decode the messages of the other
users. This design constraint is responsible for the multiplexing gain erosion,
rate loss, and inefficient use of SIC receivers in multi-antenna settings. Our
results confirm that NOMA should not be applied blindly to multi-antenna
settings, highlight the scenarios where MULP outperforms NOMA and vice versa,
and demonstrate the inefficiency, performance loss and complexity disadvantages
of NOMA compared to RS. The first takeaway message is that, while NOMA is not
beneficial in most multi-antenna deployments. The second takeaway message is
that other non-orthogonal transmission frameworks, such as RS, exist which
fully exploit the multiplexing gain and the benefits of SIC to boost the rate
in multi-antenna settings.


Abstract:
  We consider representation learning from 3D graphs in which each node is
associated with a spatial position in 3D. This is an under explored area of
research, and a principled framework is currently lacking. In this work, we
propose a generic framework, known as the 3D graph network (3DGN), to provide a
unified interface at different levels of granularity for 3D graphs. Built on
3DGN, we propose the spherical message passing (SMP) as a novel and specific
scheme for realizing the 3DGN framework in the spherical coordinate system
(SCS). We conduct formal analyses and show that the relative location of each
node in 3D graphs is uniquely defined in the SMP scheme. Thus, our SMP
represents a complete and accurate architecture for learning from 3D graphs in
the SCS. We derive physically-based representations of geometric information
and propose the SphereNet for learning representations of 3D graphs. We show
that existing 3D deep models can be viewed as special cases of the SphereNet.
Experimental results demonstrate that the use of complete and accurate 3D
information in 3DGN and SphereNet leads to significant performance improvements
in prediction tasks.


Abstract:
  Many real-world classification problems often have classes with very few
labeled training samples. Moreover, all possible classes may not be initially
available for training, and may be given incrementally. Deep learning models
need to deal with this two-fold problem in order to perform well in real-life
situations. In this paper, we propose a novel Few-Shot Lifelong Learning (FSLL)
method that enables deep learning models to perform lifelong/continual learning
on few-shot data. Our method selects very few parameters from the model for
training every new set of classes instead of training the full model. This
helps in preventing overfitting. We choose the few parameters from the model in
such a way that only the currently unimportant parameters get selected. By
keeping the important parameters in the model intact, our approach minimizes
catastrophic forgetting. Furthermore, we minimize the cosine similarity between
the new and the old class prototypes in order to maximize their separation,
thereby improving the classification performance. We also show that integrating
our method with self-supervision improves the model performance significantly.
We experimentally show that our method significantly outperforms existing
methods on the miniImageNet, CIFAR-100, and CUB-200 datasets. Specifically, we
outperform the state-of-the-art method by an absolute margin of 19.27% for the
CUB dataset.


Abstract:
  Fraud acts as a major deterrent to a companys growth if uncontrolled. It
challenges the fundamental value of Trust in the Insurance business. COVID-19
brought additional challenges of increased potential fraud to health insurance
business. This work describes implementation of existing and enhanced fraud
detection methods in the pre-COVID-19 and COVID-19 environments. For this
purpose, we have developed an innovative enhanced fraud detection framework
using actuarial and data science techniques. Triggers specific to COVID-19 are
identified in addition to the existing triggers. We have also explored the
relationship between insurance fraud and COVID-19. To determine this we
calculated Pearson correlation coefficient and fitted logarithmic regression
model between fraud in health insurance and COVID-19 cases. This work uses two
datasets: health insurance dataset and Kaggle dataset on COVID-19 cases for the
same select geographical location in India. Our experimental results shows
Pearson correlation coefficient of 0.86, which implies that the month on month
rate of fraudulent cases is highly correlated with month on month rate of
COVID-19 cases. The logarithmic regression performed on the data gave the
r-squared value of 0.91 which indicates that the model is a good fit. This work
aims to provide much needed tools and techniques for health insurance business
to counter the fraud.


Abstract:
  It is challenging to deal with censored data, where we only have access to
the incomplete information of survival time instead of its exact value.
Fortunately, under linear predictor assumption, people can obtain guaranteed
coverage for the confidence band of survival time using methods like Cox
Regression. However, when relaxing the linear assumption with neural networks
(e.g., Cox-MLP (Katzman et al., 2018; Kvamme et al., 2019)), we lose the
guaranteed coverage. To recover the guaranteed coverage without linear
assumption, we propose two algorithms based on conformal inference. In the
first algorithm WCCI, we revisit weighted conformal inference and introduce a
new non-conformity score based on partial likelihood. We then propose a
two-stage algorithm T-SCI, where we run WCCI in the first stage and apply
quantile conformal inference to calibrate the results in the second stage.
Theoretical analysis shows that T-SCI returns guaranteed coverage under milder
assumptions than WCCI. We conduct extensive experiments on synthetic data and
real data using different methods, which validate our analysis.


Abstract: A complex function is associated to each bounded linear operator


Abstract:
  In Heisenberg models with exchange anisotropy, transverse spin components are
not conserved and can decay not only by transport, but also by dephasing. Here
we utilize ultracold atoms to simulate the dynamics of 1D Heisenberg spin
chains, and observe fast, local spin decay controlled by the anisotropy.
Additionally, we directly observe an effective magnetic field created by
superexchange which causes an inhomogeneous decay mechanism due to variations
of lattice depth between chains, as well as dephasing within each chain due to
the twofold reduction of the effective magnetic field at the edges of the
chains and due to fluctuations of the effective magnetic field in the presence
of mobile holes. The latter is a new coupling mechanism between holes and
magnons. All these dephasing mechanisms, corroborated by extensive numerical
simulations, have not been observed before with ultracold atoms and illustrate
basic properties of the underlying Hubbard model.


Abstract:
  Let $K$ be a field. Let $f\in K[[x_{1},...,x_{r}]]$ and $g\in
K[[y_{1},...,y_{s}]]$ be nonzero elements. If $X$ (resp. $Y$) is a matrix
factorization of $f$ (resp. $g$), Yoshino had constructed a tensor product (of
matrix factorizations) $\widehat{\otimes}$ such that $X\widehat{\otimes}Y$ is a
matrix factorization of $f+g\in K[[x_{1},...,x_{r},y_{1},...,y_{s}]]$. In this
paper, we propose a bifunctorial operation $\widetilde{\otimes}$ and its
variant $\widetilde{\otimes}'$ such that $X\widetilde{\otimes}Y$ and
$X\widetilde{\otimes}' Y$ are two different matrix factorizations of $fg\in
K[[x_{1},...,x_{r},y_{1},...,y_{s}]]$. We call $\widetilde{\otimes}$ the
multiplicative tensor product of $X$ and $Y$. Several properties of
$\widetilde{\otimes}$ are proved. Moreover, we find three functorial variants
of Yoshino's tensor product $\widehat{\otimes}$. Then, $\widetilde{\otimes}$
(or its variant) is used in conjunction with $\widehat{\otimes}$ (or any of its
variants) to give an improved version of the standard algorithm for factoring
polynomials using matrices on the class of summand-reducible polynomials
defined in this paper. Our algorithm produces matrix factors whose size is at
most one half the size one obtains using the standard method.


Abstract:
  Natural forking in blockchain refers to a phenomenon that there are a set of
blocks at one block height at the same time, implying that various nodes have
different perspectives of the main chain. Natural forking might give rise to
multiple adverse impacts on blockchain, jeopardizing the performance and
security of the system consequently. However, the ongoing literature in
analyzing natural forking is mainly from the macro point of view, which is not
sufficient to incisively understand this phenomenon. In this paper, we fill
this gap through leveraging the large deviation theory to conduct a microscopic
study of natural forking, which resorts to investigating the instantaneous
difference between block generation and dissemination in blockchain. Our work
is derived comprehensively and complementarily via a three-step process, where
both the natural forking probability and its decay rate are presented. Through
solid theoretical derivation and extensive numerical simulations, we find 1)
the probability of the mismatch between block generation and dissemination
exceeding a given threshold dwindles exponentially with the increase of natural
forking robustness related parameter or the difference between the block
dissemination rate and block creation rate; 2) the natural forking robustness
related parameter may emphasize a more dominant effect on accelerating the
abortion of natural forking in some cases; 3) when the self-correlated block
generation rate is depicted as the stationary autoregressive process with a
scaling parameter, it is found that setting a lower scaling parameter may speed
up the failure of natural forking. These findings are valuable since they offer
a fresh theoretical basis to engineer optimal countermeasures for thwarting
natural forking and thereby enlivening the blockchain network.


Abstract:
  The integration of multi-access edge computing (MEC) and RAFT consensus makes
it feasible to deploy blockchain on trustful base stations and gateways to
provide efficient and tamper-proof edge data services for Internet of Things
(IoT) applications. However, reducing the latency of storing data on blockchain
remains a challenge, especially when an anomalytriggered data flow in a certain
area exceeds the block generation speed. This letter proposes an intelligent
transaction migration scheme for RAFT-based private blockchain in IoT
applications to migrate transactions in busy areas to idle regions
intelligently. Simulation results show that the proposed scheme can apparently
reduce the latency in high data flow circumstances.


Abstract:
  (RFT) allows very high-Q active mode resonators, promising crystal-less
monolithic clock generation for mmWave systems. However, there is a strong need
for design of mmWave oscillators that utilize the high-Q of active-mode RFT
(AM-RFT) optimally, while handling unique challenges such as resonator's low
electromechanical transduction. In this brief, we develop a theory and through
design and post-layout simulations in 14 nm Global Foundry process, we show the
first active oscillator with AM-RFT at 30 GHz, which improves the fundamental
limits of phase noise and figure-of-merit as compared to the oscillators with
conventional LC resonators. For AM-RFT with Q factor of 10K, post layout
simulation results show that the proposed oscillator exhibits phase noise less
than -140 dBc per Hz and figure-of-merit greater than 228 dBc per Hz at 1 MHz
offset for 30 GHz center frequency, which are more than 25 dB better than the
existing monolithic LC oscillators.


Abstract:
  We study the question for which Tychonoff spaces $X$ and locally convex
spaces $E$ the space $C_p(X,E)$ of continuous $E$-valued functions on $X$
contains a complemented copy of the space
$(c_0)_p=\{x\in\mathbb{R}^\omega\colon x(n)\to0\}$, both endowed with the
pointwise topology. We provide a positive answer for a vast class of spaces,
extending classical theorems of Cembranos, Freniche, and Doma\'nski and
Drewnowski, proved for the case of Banach and Fr\'echet spaces $C_k(X,E)$. For
given infinite Tychonoff spaces $X$ and $Y$, using the results of Henriksen and
Woods and analysing relations between the space $C_p(X,C_p(Y))$ and the space
$SC_p(X\times Y)$ of separately continuous real-valued functions on the product
$X\times Y$, we show that $C_p(X,C_p(Y))$ contains a complemented copy of
$(c_0)_p$ if and only if any of the spaces $C_p(X)$ and $C_p(Y)$ contains such
a subspace. This approach applies also to show that, contrary to the case of
the compact-open topology, for every infinite compact spaces $X$ and $Y$ the
space $C_p(X\times Y)$ cannot be mapped onto the space $C_p(X,C_p(Y))$ by a
continuous linear operator.


Abstract:
  Tensor-based methods have been widely studied to attack inverse problems in
hyperspectral imaging since a hyperspectral image (HSI) cube can be naturally
represented as a third-order tensor, which can perfectly retain the spatial
information in the image. In this article, we extend the linear tensor method
to the nonlinear tensor method and propose a nonlinear low-rank tensor unmixing
algorithm to solve the generalized bilinear model (GBM). Specifically, the
linear and nonlinear parts of the GBM can both be expressed as tensors.
Furthermore, the low-rank structures of abundance maps and nonlinear
interaction abundance maps are exploited by minimizing their nuclear norm, thus
taking full advantage of the high spatial correlation in HSIs. Synthetic and
real-data experiments show that the low rank of abundance maps and nonlinear
interaction abundance maps exploited in our method can improve the performance
of the nonlinear unmixing. A MATLAB demo of this work will be available at
https://github.com/LinaZhuang for the sake of reproducibility.


Abstract:
  Even though machine learning algorithms already play a significant role in
data science, many current methods pose unrealistic assumptions on input data.
The application of such methods is difficult due to incompatible data formats,
or heterogeneous, hierarchical or entirely missing data fragments in the
dataset. As a solution, we propose a versatile, unified framework called
`HMill' for sample representation, model definition and training. We review in
depth a multi-instance paradigm for machine learning that the framework builds
on and extends. To theoretically justify the design of key components of HMill,
we show an extension of the universal approximation theorem to the set of all
functions realized by models implemented in the framework. The text also
contains a detailed discussion on technicalities and performance improvements
in our implementation, which is published for download under the MIT License.
The main asset of the framework is its flexibility, which makes modelling of
diverse real-world data sources with the same tool possible. Additionally to
the standard setting in which a set of attributes is observed for each object
individually, we explain how message-passing inference in graphs that represent
whole systems of objects can be implemented in the framework. To support our
claims, we solve three different problems from the cybersecurity domain using
the framework. The first use case concerns IoT device identification from raw
network observations. In the second problem, we study how malicious binary
files can be classified using a snapshot of the operating system represented as
a directed graph. The last provided example is a task of domain blacklist
extension through modelling interactions between entities in the network. In
all three problems, the solution based on the proposed framework achieves
performance comparable to specialized approaches.


Abstract:
  Recently, deep neural networks have become to be used in a variety of
applications. While the accuracy of deep neural networks is increasing, the
confidence score, which indicates the reliability of the prediction results, is
becoming more important. Deep neural networks are seen as highly accurate but
known to be overconfident, making it important to calibrate the confidence
score. Many studies have been conducted on confidence calibration. They
calibrate the confidence score of the model to match its accuracy, but it is
not clear whether these confidence scores can improve the performance of
systems that use confidence scores. This paper focuses on cascade inference
systems, one kind of systems using confidence scores, and discusses the desired
confidence score to improve system performance in terms of inference accuracy
and computational cost. Based on the discussion, we propose a new confidence
calibration method, Learning to Cascade. Learning to Cascade is a simple but
novel method that optimizes the loss term for confidence calibration
simultaneously with the original loss term. Experiments are conducted using two
datasets, CIFAR-100 and ImageNet, in two system settings, and show that naive
application of existing calibration methods to cascade inference systems
sometimes performs worse. However, Learning to Cascade always achieves a better
trade-off between inference accuracy and computational cost. The simplicity of
Learning to Cascade allows it to be easily applied to improve the performance
of existing systems.


Abstract:
  Imbalanced datasets widely exist in practice and area great challenge for
training deep neural models with agood generalization on infrequent classes. In
this work, wepropose a new rare-class sample generator (RSG) to solvethis
problem. RSG aims to generate some new samplesfor rare classes during training,
and it has in particularthe following advantages: (1) it is convenient to use
andhighly versatile, because it can be easily integrated intoany kind of
convolutional neural network, and it works wellwhen combined with different
loss functions, and (2) it isonly used during the training phase, and
therefore, no ad-ditional burden is imposed on deep neural networks duringthe
testing phase. In extensive experimental evaluations, weverify the
effectiveness of RSG. Furthermore, by leveragingRSG, we obtain competitive
results on Imbalanced CIFARand new state-of-the-art results on Places-LT,
ImageNet-LT, and iNaturalist 2018. The source code is available at
https://github.com/Jianf-Wang/RSG.


Abstract:
  Weaving frames in separable Hilbert spaces have been recently introduced by
Bemrose et al. to deal with some problems in distributed signal processing and
wireless sensor networks. In this paper, we study the notion of excess for
woven frames and prove that any two frames in a separable Hilbert space that
are woven have the same excess. We also show that every frame with a large
class of duals is woven provided that its redundant elements have small enough
norm. Also, we try to transfer the woven property from frames to their duals
and vise versa. Finally, we look at which perturbations of dual frames preserve
the woven property, moreover it is shown that under some conditions the
canonical Pareseval frame of two woven frames are also woven.


Abstract:
  Detecting and responding to novel situations in open-world environments is a
key capability of human cognition. Current artificial intelligence (AI)
researchers strive to develop systems that can perform in open-world
environments. Novelty detection is an important ability of such AI systems. In
an open-world, novelties appear in various forms and the difficulty to detect
them varies. Therefore, to accurately evaluate the detection capability of AI
systems, it is necessary to investigate the difficulty to detect novelties. In
this paper, we propose a qualitative physics-based method to quantify the
difficulty of novelty detection focusing on open-world physical domains. We
apply our method in a popular physics simulation game, Angry Birds. We conduct
an experiment with human players with different novelties in Angry Birds to
validate our method. Results indicate that the calculated difficulty values are
in line with the detection difficulty of the human players.


Abstract:
  Digital fabrication courses that relied on physical makerspaces were severely
disrupted by COVID-19. As universities shut down in Spring 2020, instructors
developed new models for digital fabrication at a distance. Through interviews
with faculty and students and examination of course materials, we recount the
experiences of eight remote digital fabrication courses. We found that learning
with hobbyist equipment and online social networks could emulate using
industrial equipment in shared workshops. Furthermore, at-home digital
fabrication offered unique learning opportunities including more iteration,
machine tuning, and maintenance. These opportunities depended on new forms of
labor and varied based on student living situations. Our findings have
implications for remote and in-person digital fabrication instruction. They
indicate how access to tools was important, but not as critical as providing
opportunities for iteration; they show how remote fabrication exacerbated
student inequities; and they suggest strategies for evaluating trade-offs in
remote fabrication models with respect to learning objectives.


Abstract:
  We solve the pointwise Landau-Kolmogorov problem on the interval $\mathbb{I}
= [-1,1]$ on finding $\left|f^{(k)}(t)\right|\to\sup$ under constraints
$\|f\|_2 \leqslant \delta$ and $\left\|f^{(r)}\right\|_2\leqslant 1$, where
$t\in\mathbb{I}$ and $\delta > 0$ are fixed. For $r = 1$ and $r = 2$, we solve
the uniform version of the Landau-Kolmogorov problem on the interval
$\mathbb{I}$ in the Taikov case by proving the Karlin-type conjecture
$\sup\limits_{t\in \mathbb{I}}\left|f^{(k)}(t)\right| =
\left|f^{(k)}(-1)\right|$ under above constraints. The proof relies on the
analysis of the dependence of the norm of the solution to higher-order
Sturm-Liouville equation $(-1)^ru^{(2r)} + \lambda u = -\lambda f$ with
boundary conditions $u^{(s)}(-1) = u^{(s)}(1) = 0$, $s = 0,1,\ldots,r-1$, on
non-negative parameter $\lambda$, where $f$ is some piece-wise polynomial
function. Furthermore, we find sharp inequality $\left\|f^{(k)}\right\|_\infty
\leqslant A\|f\|_2 + B\left\|f^{(r)}\right\|_2$ with the smallest possible
constant $A > 0$ and the smallest possible constant $B = B(A)$ for $k \in
\{r-2, r-1\}$.


Abstract:
  Pairwise comparison matrices have received substantial attention in a variety
of applications, especially in rank aggregation, the task of flattening items
into a one-dimensional (and thus transitive) ranking. However, non-transitive
preference cycles can arise in practice due to the fact that making a decision
often requires a complex evaluation of multiple factors. In some applications,
it may be important to identify and preserve information about the inherent
non-transitivity, either in the pairwise comparison data itself or in the
latent feature space. In this work, we develop structured models for
non-transitive pairwise comparison matrices that can be exploited to recover
such matrices from incomplete noisy data and thus allow the detection of
non-transitivity. Considering that individuals' tastes and items' latent
features may change over time, we formulate time-varying pairwise comparison
matrix recovery as a dynamic skew-symmetric matrix recovery problem by modeling
changes in the low-rank factors of the pairwise comparison matrix. We provide
theoretical guarantees for the recovery and numerically test the proposed
theory with both synthetic and real-world data.


Abstract:
  Stochastic gravitational wave background (SGWB) is a promising tool to probe
the very early universe where the standard model of particle physics and
cosmology are connected closely. As a possible component of SGWB, gravitational
waves (GW) from bubble collisions during the first order cosmological phase
transitions deserve comprehensive analyses. In 2017, Ryusuke Jinno and Masahiro
Takimoto proposed an elegant analysis approach to derive the analytical
expressions of energy spectra of GW from bubble collisions in Minkowski
spacetime avoiding large-scale numerical simulations for the first time[1].
However, they neglect the expansion of the universe and regard the duration of
phase transitions as infinity in their derivation which could deviate their
estimations from true values. For these two reasons, we give a new expression
of GW spectra by adopting their method, switching spacetime background to FLRW
spacetime and considering a finite duration of phase transitions. By denoting
$\sigma$ as the fraction of the speed of phase transitions to the expansion
speed of the universe, we find for different $\sigma$, the maxima of GW energy
spectra drop by around 1-3 orders of magnitude than the results given by their
previous work. Such a significant decrease may bring about new challenges for
detectability of GW from bubble collisions. Luckily, by comparing new spectra
with PLI (power-law integrated) sensitivity curves of GW detectors, we find GW
from bubble collisions still could be detectable for BBO and LISA in future
detection.


Abstract:
  We propose a conjectural construction of global points on modular elliptic
curves over arbitrary number fields, generalizing both the p-adic construction
of Heegner points via Cerednik-Drinfeld uniformization and the definition of
classical Stark-Heegner points. In alignment with Nekovar and Scholl's plectic
conjectures, we expect the non-triviality of these plectic Stark-Heegner points
to control the Mordell-Weil group of higher rank elliptic curves. We provide
some indirect evidence for our conjectures by showing that higher order
derivatives of anticyclotomic p-adic L-functions compute plectic invariants.


Abstract:
  Continuous deep learning architectures enable learning of flexible
probabilistic models for predictive modeling as neural ordinary differential
equations (ODEs), and for generative modeling as continuous normalizing flows.
In this work, we design a framework to decipher the internal dynamics of these
continuous depth models by pruning their network architectures. Our empirical
results suggest that pruning improves generalization for neural ODEs in
generative modeling. We empirically show that the improvement is because
pruning helps avoid mode-collapse and flatten the loss surface. Moreover,
pruning finds efficient neural ODE representations with up to 98% less
parameters compared to the original network, without loss of accuracy. We hope
our results will invigorate further research into the performance-size
trade-offs of modern continuous-depth models.


Abstract:
  It is evident that deep text classification models trained on human data
could be biased. In particular, they produce biased outcomes for texts that
explicitly include identity terms of certain demographic groups. We refer to
this type of bias as explicit bias, which has been extensively studied.
However, deep text classification models can also produce biased outcomes for
texts written by authors of certain demographic groups. We refer to such bias
as implicit bias of which we still have a rather limited understanding. In this
paper, we first demonstrate that implicit bias exists in different text
classification tasks for different demographic groups. Then, we build a
learning-based interpretation method to deepen our knowledge of implicit bias.
Specifically, we verify that classifiers learn to make predictions based on
language features that are related to the demographic attributes of the
authors. Next, we propose a framework Debiased-TC to train deep text
classifiers to make predictions on the right features and consequently mitigate
implicit bias. We conduct extensive experiments on three real-world datasets.
The results show that the text classification models trained under our proposed
framework outperform traditional models significantly in terms of fairness, and
also slightly in terms of classification performance.


Abstract:
  We consider convolutional neural network (CNN) ensemble learning with the
objective function inspired by least action principle; it includes resource
consumption component. We teach an agent to perceive images through the set of
pre-trained classifiers and want the resulting dynamically configured system to
unfold the computational graph with the trajectory that refers to the minimal
number of operations and maximal expected accuracy. The proposed agent's
architecture implicitly approximates the required classifier selection function
with the help of reinforcement learning. Our experimental results prove, that
if the agent exploits the dynamic (and context-dependent) structure of
computations, it outperforms conventional ensemble learning.


Abstract:
  This paper presents a novel framework for unsupervised anomaly detection on
masked objects called ODDObjects, which stands for Out-of-Distribution
Detection on Objects. ODDObjects is designed to detect anomalies of various
categories using unsupervised autoencoders trained on COCO-style datasets. The
method utilizes autoencoder-based image reconstruction, where high
reconstruction error indicates the possibility of an anomaly. The framework
extends previous work on anomaly detection with autoencoders, comparing
state-of-the-art models trained on object recognition datasets. Various model
architectures were compared, and experimental results show that
memory-augmented deep convolutional autoencoders perform the best at detecting
out-of-distribution objects.


Abstract:
  Biological and physical systems that can be classified as oscillatory media
give rise to interesting phenomena like target patterns and spiral waves. The
existence of these structures has been proven in the case of systems with local
diffusive interactions. In this paper the more general case of oscillatory
media with nonlocal coupling is considered. We model these systems using
evolution equations where the nonlocal interactions are expressed via a
diffusive convolution kernel, and prove the existence of rotating wave
solutions for these systems. Since the nonlocal nature of the equations
precludes the use of standard techniques from spatial dynamics, the method we
use relies instead on a combination of a multiple-scales analysis and a
construction similar to Lyapunov-Schmidt. This approach then allows us to
derive a normal form, or reduced equation, that captures the leading order
behavior of these solutions.


Abstract:
  In this paper, we will provide constructions of D-module structures on the
complex computing the periodic cyclic homology of an $A$-linear stable
infinity-category in characteristic zero. We give two methods. The first one is
based on a canonical extension of factorization homology to the mapping stack
and relation between sheaves on free loop space and D-modules. The second one
uses the algebraic structure on Hochschild pairs, its moduli-theoretic
interpretation, Kodaira-Spencer morphisms, and the relation between dg Lie
algebras and pointed formal stacks.


Abstract:
  Maximum distance separable (MDS) codes are very important in both theory and
practice. There is a classical construction of a family of $[2^m+1, 2u-1,
2^m-2u+3]$ MDS codes for $1 \leq u \leq 2^{m-1}$, which are cyclic, reversible
and BCH codes over $\mathrm{GF}(2^m)$. The objective of this paper is to study
the quaternary subfield subcodes and quaternary subfield codes of a subfamily
of the MDS codes for even $m$. A family of quaternary cyclic codes is obtained.
These quaternary codes are distance-optimal in some cases and very good in
general. Furthermore, infinite families of $3$-designs from these quaternary
codes are presented.


Abstract:
  We will introduce the notion of strong Morita equivalence for completely
positive linear maps and study its basic properties. Also, we will discuss the
relation between strong Morita equivalence for bounded $C^*$-bimodule linear
maps and strong Morita equivalence for completely positive linear maps.
Furthermore, we will show that if two unital $C^*$-algebras are strongly Morita
equivalent, then there is a $1-1$ correspondence between the two sets of all
strong Morita equivalence classes of completely positive linear maps on the two
unital $C^*$-algebras and we will show that the corresponding two classes of
the completely positive linear maps are also strongly Morita equivalent.


Abstract:
  An open problem posed by Milner asks for a proof that a certain
axiomatisation, which Milner showed is sound with respect to bisimilarity for
regular expressions, is also complete. One of the main difficulties of the
problem is the lack of a full Kleene theorem, since there are automata that can
not be specified, up to bisimilarity, by an expression. Grabmayer and Fokkink
(2020) characterise those automata that can be expressed by regular expressions
without the constant 1, and use this characterisation to give a positive answer
to Milner's question for this subset of expressions. In this paper, we analyse
Grabmayer and Fokkink's proof of completeness from the perspective of universal
coalgebra, and thereby give an abstract account of their proof method. We then
compare this proof method to another approach to completeness proofs from
coalgebraic language theory. This culminates in two abstract proof methods for
completeness, what we call the local and global approaches, and a description
of when one method can be used in place of the other.


Abstract:
  We study the collision between the cue and the ball in the game of billiards.
After studying the collision process in detail, we write the (rotational)
velocities of the ball and the cue after the collision. We also find the squirt
angle of the ball for an oblique collision which represents the deviation of
the ball from the intended direction.


Abstract:
  The recurrent mechanism has recently been introduced into U-Net in various
medical image segmentation tasks. Existing studies have focused on promoting
network recursion via reusing building blocks. Although network parameters
could be greatly saved, computational costs still increase inevitably in
accordance with the pre-set iteration time. In this work, we study a
multi-scale upgrade of a bi-directional skip connected network and then
automatically discover an efficient architecture by a novel two-phase Neural
Architecture Search (NAS) algorithm, namely BiX-NAS. Our proposed method
reduces the network computational cost by sifting out ineffective multi-scale
features at different levels and iterations. We evaluate BiX-NAS on two
segmentation tasks using three different medical image datasets, and the
experimental results show that our BiX-NAS searched architecture achieves the
state-of-the-art performance with significantly lower computational cost.


Abstract:
  In the first half of twentieth century the theory of complex analytic
functions and of their zerosets was fully developed. The definition of
holomorphic function has a local nature. Germs of holomorphic functions form a
distinguished subring of the ring of germs of continuous functions. This way
came out the notion of analytic space. The definition of a complex analytic set
is by local models as in the case of complex manifolds. But while local models
for manifolds are open sets of ${\mathbb C}^n$, a local model of an analytic
space is the zeroset of finitely many analytic functions on an open set of
${\mathbb C}^n$ together with a sheaf of continuous function to be called
holomorphic.
  Towards the years $50$ of the last century, Cartan, Whitney, Bruhat and
others tried to formulate the notion of analytic space over ${\mathbb R}$.
Immediately they realize that the real sets verifying a definition similar to
the complex one form a cathegory whose elements can get unpleasant behaviour.
In particular this cathegory does not get the good properties of complex
analytic spaces, as for instance coherence of their structural sheaves and
Theorems A and B do not hold in general.


Abstract:
  In this paper, we present a Symbolic Reinforcement Learning (SRL) based
architecture for safety control of Radio Access Network (RAN) applications. In
particular, we provide a purely automated procedure in which a user can specify
high-level logical safety specifications for a given cellular network topology
in order for the latter to execute optimal safe performance which is measured
through certain Key Performance Indicators (KPIs). The network consists of a
set of fixed Base Stations (BS) which are equipped with antennas, which one can
control by adjusting their vertical tilt angle. The aforementioned process is
called Remote Electrical Tilt (RET) optimization. Recent research has focused
on performing this RET optimization by employing Reinforcement Learning (RL)
strategies due to the fact that they have self-learning capabilities to adapt
in uncertain environments. The term safety refers to particular constraints
bounds of the network KPIs in order to guarantee that when the algorithms are
deployed in a live network, the performance is maintained. In our proposed
architecture the safety is ensured through model-checking techniques over
combined discrete system models (automata) that are abstracted through the
learning process. We introduce a user interface (UI) developed to help a user
set intent specifications to the system, and inspect the difference in agent
proposed actions, and those that are allowed and blocked according to the
safety specification.


Abstract:
  Given a graph $G$ whose edges are labeled by ideals of a commutative ring $R$
with identity, a generalized spline is a vertex labeling of $G$ by the elements
of $R$ so that the difference of labels on adjacent vertices is an element of
the corresponding edge ideal. The set of all generalized splines on a graph $G$
with base ring $R$ has a ring and an $R$-module structure.
  In this paper, we focus on the freeness of generalized spline modules over
certain graphs with the base ring $R = k[x_1 , \ldots , x_d]$ where $k$ is a
field. We first show the freeness of generalized spline modules on graphs with
no interior edges over $k[x,y]$ such as cycles or a disjoint union of cycles
with free edges. Later, we consider graphs that can be decomposed into disjoint
cycles without changing the isomorphism class of the syzygy modules. Then we
use this decomposition to show that generalized spline modules are free over
$k[x , y]$ and later we extend this result to the base ring $R = k[x_1 , \ldots
, x_d]$ under some restrictions.


Abstract:
  Despite the significant progress over the last 50 years in simulating flow
problems using numerical discretization of the Navier-Stokes equations (NSE),
we still cannot incorporate seamlessly noisy data into existing algorithms,
mesh-generation is complex, and we cannot tackle high-dimensional problems
governed by parametrized NSE. Moreover, solving inverse flow problems is often
prohibitively expensive and requires complex and expensive formulations and new
computer codes. Here, we review flow physics-informed learning, integrating
seamlessly data and mathematical models, and implementing them using
physics-informed neural networks (PINNs). We demonstrate the effectiveness of
PINNs for inverse problems related to three-dimensional wake flows, supersonic
flows, and biomedical flows.


Abstract:
  Text classification is a widely studied problem and has broad applications.
In many real-world problems, the number of texts for training classification
models is limited, which renders these models prone to overfitting. To address
this problem, we propose SSL-Reg, a data-dependent regularization approach
based on self-supervised learning (SSL). SSL is an unsupervised learning
approach which defines auxiliary tasks on input data without using any
human-provided labels and learns data representations by solving these
auxiliary tasks. In SSL-Reg, a supervised classification task and an
unsupervised SSL task are performed simultaneously. The SSL task is
unsupervised, which is defined purely on input texts without using any
human-provided labels. Training a model using an SSL task can prevent the model
from being overfitted to a limited number of class labels in the classification
task. Experiments on 17 text classification datasets demonstrate the
effectiveness of our proposed method.


Abstract:
  Epilepsy is a chronic neurological disorder affecting more than 50 million
people globally. An epileptic seizure acts like a temporary shock to the
neuronal system, disrupting normal electrical activity in the brain. Epilepsy
is frequently diagnosed with electroencephalograms (EEGs). Current methods
study the time-varying spectra and coherence but do not directly model changes
in extreme behavior. Thus, we propose a new approach to characterize brain
connectivity based on the joint tail behavior of the EEGs. Our proposed method,
the conditional extremal dependence for brain connectivity (Conex-Connect), is
a pioneering approach that links the association between extreme values of
higher oscillations at a reference channel with the other brain network
channels. Using the Conex-Connect method, we discover changes in the extremal
dependence driven by the activity at the foci of the epileptic seizure. Our
model-based approach reveals that, pre-seizure, the dependence is notably
stable for all channels when conditioning on extreme values of the focal
seizure area. Post-seizure, by contrast, the dependence between channels is
weaker, and dependence patterns are more "chaotic". Moreover, in terms of
spectral decomposition, we find that high values of the high-frequency
Gamma-band are the most relevant features to explain the conditional extremal
dependence of brain connectivity.


Abstract:
  We consider a cosmological model with dark matter in the form of
$\sim10^{-12}M_\odot$ primordial black holes in dense weakly relativistic
clusters with masses $18-560M_\odot$. It is shown that during the multiple
collisions of the black holes the $\sim10$\% of the initial cluster mass can be
transformed into gravitational waves in the time interval from recombination to
the redshifts $z\geq 10$. At the recombination epoch, the density of matter was
larger by $\sim10$\% and, accordingly, the universe expansion rate was higher.
This leads to a shortening of the sound horizon scale, as is necessary to solve
the "$H_0$ tension" problem.


Abstract:
  Whitney proved in 1931 that every 4-connected planar triangulation is
hamiltonian. Later in 1979, Hakimi, Schmeichel and Thomassen conjectured that
every such triangulation on $n$ vertices has at least $2(n - 2)(n - 4)$
hamiltonian cycles. Along this direction, Brinkmann, Souffriau and Van Cleemput
established a linear lower bound on the number of hamiltonian cycles in
4-connected planar triangulations. In stark contrast, Alahmadi, Aldred and
Thomassen showed that every 5-connected triangulation of the plane or the
projective plane has exponentially many hamiltonian cycles. This gives the
motivation to study the number of hamiltonian cycles of 4-connected
triangulations with few 4-separators. Recently, Liu and Yu showed that every
4-connected planar triangulation with $O(n / \log n)$ 4-separators has a
quadratic number of hamiltonian cycles. By adapting the framework of Alahmadi
et al. we strengthen the last two aforementioned results. We prove that every
4-connected planar or projective planar triangulation with $O(n)$ 4-separators
has exponentially many hamiltonian cycles.


Abstract:
  Recent theoretical works on effective, four-band models of three-dimensional,
Dirac semimetals suggest the generic planes in momentum space, orthogonal to
the direction of nodal separation, and lying between two Dirac points are
higher-order topological insulators, supporting gapped, edge-states.
Furthermore, the second homotopy classification of four-band models shows the
higher-order topological insulators support quantized, non-Abelian Berry's flux
and the Dirac points are monopoles of $SO(5)$ Berry's connections. Due to the
lack of suitable computational scheme, such bulk topological properties are yet
to be determined from the \emph{ab initio} band structures of Dirac materials.
In this work, we report first, comprehensive topological classification of
\emph{ab initio} band structures of Na$_3$Bi, by computing Wilson loops of
non-Abelian, Berry's connections for several, Kramers-degenerate bands. Our
work shows the quantized, non-Abelian, Berry's flux can be used as a stable,
bulk invariant for describing higher-order topology and topological phase
transitions.


Abstract:
  Encoder pre-training is promising in end-to-end Speech Translation (ST),
given the fact that speech-to-translation data is scarce. But ST encoders are
not simple instances of Automatic Speech Recognition (ASR) or Machine
Translation (MT) encoders. For example, we find that ASR encoders lack the
global context representation, which is necessary for translation, whereas MT
encoders are not designed to deal with long but locally attentive acoustic
sequences. In this work, we propose a Stacked Acoustic-and-Textual Encoding
(SATE) method for speech translation. Our encoder begins with processing the
acoustic sequence as usual, but later behaves more like an MT encoder for a
global representation of the input sequence. In this way, it is straightforward
to incorporate the pre-trained models into the system. Also, we develop an
adaptor module to alleviate the representation inconsistency between the
pre-trained ASR encoder and MT encoder, and develop a multi-teacher knowledge
distillation method to preserve the pre-training knowledge. Experimental
results on the LibriSpeech En-Fr and MuST-C En-De ST tasks show that our method
achieves state-of-the-art BLEU scores of 18.3 and 25.2. To our knowledge, we
are the first to develop an end-to-end ST system that achieves comparable or
even better BLEU performance than the cascaded ST counterpart when large-scale
ASR and MT data is available.


Abstract:
  Cardiac auscultation is an essential point-of-care method used for the early
diagnosis of heart diseases. Automatic analysis of heart sounds for abnormality
detection is faced with the challenges of additive noise and sensor-dependent
degradation. This paper aims to develop methods to address the cardiac
abnormality detection problem when both types of distortions are present in the
cardiac auscultation sound. We first mathematically analyze the effect of
additive and convolutional noise on short-term filterbank-based features and a
Convolutional Neural Network (CNN) layer. Based on the analysis, we propose a
combination of linear and logarithmic spectrogram-image features. These 2D
features are provided as input to a residual CNN network (ResNet) for heart
sound abnormality detection. Experimental validation is performed on an
open-access heart sound abnormality detection dataset involving noisy
recordings obtained from multiple stethoscope sensors. The proposed method
achieves significantly improved results compared to the conventional
approaches, with an area under the ROC (receiver operating characteristics)
curve (AUC) of 91.36%, F-1 score of 84.09%, and Macc (mean of sensitivity and
specificity) of 85.08%. We also show that the proposed method shows the best
mean accuracy across different source domains including stethoscope and noise
variability, demonstrating its effectiveness in different recording conditions.
The proposed combination of linear and logarithmic features along with the
ResNet classifier effectively minimizes the impact of background noise and
sensor variability for classifying phonocardiogram (PCG) signals. The proposed
method paves the way towards developing computer-aided cardiac auscultation
systems in noisy environments using low-cost stethoscopes.


Abstract:
  We present a first 3D magnetohydrodynamic (MHD) simulation of convective
oxygen and neon shell burning in a non-rotating $18\, M_\odot$ star shortly
before core collapse to study the generation of magnetic fields in supernova
progenitors. We also run a purely hydrodynamic control simulation to gauge the
impact of the magnetic fields on the convective flow and on convective boundary
mixing. After about 17 convective turnover times, the magnetic field is
approaching saturation levels in the oxygen shell with an average field
strength of $\mathord{\sim}10^{10}\, \mathrm{G}$, and does not reach kinetic
equipartition. The field remains dominated by small to medium scales, and the
dipole field strength at the base of the oxygen shell is only $10^{9}\,
\mathrm{G}$. The angle-averaged diagonal components of the Maxwell stress
tensor mirror those of the Reynolds stress tensor, but are about one order of
magnitude smaller. The shear flow at the oxygen-neon shell interface creates
relatively strong fields parallel to the convective boundary, which noticeably
inhibit the turbulent entrainment of neon into the oxygen shell. The reduced
ingestion of neon lowers the nuclear energy generation rate in the oxygen shell
and thereby slightly slows down the convective flow. Aside from this indirect
effect, we find that magnetic fields do not appreciably alter the flow inside
the oxygen shell. We discuss the implications of our results for the subsequent
core-collapse supernova and stress the need for longer simulations, resolution
studies, and an investigation of non-ideal effects for a better understanding
of magnetic fields in supernova progenitors.


Abstract:
  Recently, $O(\alpha^3)$ corrections to the muon decay rate and
$O(\alpha_s^3)$ to heavy quark decays have been determined by Fael,
Sch\"onwald, and Steinhauser. This is the first such perturbative improvement
of these important quantities in more than two decades. We reveal and explain a
symmetry pattern in these new corrections, and confirm the most technically
difficult parts of their evaluation.


Abstract:
  We consider reaction-diffusion systems where components diffuse inside the
domain and react on the surface through mass transport type boundary conditions
on an evolving domain. Using Lyapunov functional and duality arguments, we
establish the existence of component-wise non-negative global solutions.


Abstract:
  Electron-tracking Compton camera, which is a complete Compton camera with
tracking Compton scattering electron by a gas micro time projection chamber, is
expected to open up MeV gamma-ray astronomy. The technical challenge for
achieving several degrees of the point spread function is the precise
determination of the electron-recoil direction and the scattering position from
track images. We attempted to reconstruct these parameters using convolutional
neural networks. Two network models were designed to predict the recoil
direction and the scattering position. These models marked 41$~$degrees of the
angular resolution and 2.1$~$mm of the position resolution for 75$~$keV
electron simulation data in Argon-based gas at 2$~$atm pressure. In addition,
the point spread function of ETCC was improved to 15$~$degrees from
22$~$degrees for experimental data of 662$~$keV gamma-ray source. These
performances greatly surpassed that using the traditional analysis.


Abstract:
  The simplest non-trivial 5d superconformal field theories (SCFT) are the
famous rank-one theories with $E_n$ flavour symmetry. We study their $U$-plane,
which is the one-dimensional Coulomb branch of the theory on $\mathbb{R}^4
\times S^1$. The total space of the Seiberg-Witten (SW) geometry -- the $E_n$
SW curve fibered over the $U$-plane -- is described as a rational elliptic
surface with a singular fiber of type $I_{9-n}$ at infinity. A classification
of all possible Coulomb branch configurations, for the $E_n$ theories and their
4d descendants, is given by Persson's classification of rational elliptic
surfaces. We show that the global form of the flavour symmetry group is encoded
in the Mordell-Weil group of the SW elliptic fibration. We study in detail many
special points in parameters space, such as points where the flavour symmetry
enhances, and/or where Argyres-Douglas and Minahan-Nemeschansky theories
appear. In a number of important instances, including in the massless limit,
the $U$-plane is a modular curve, and we use modularity to investigate aspects
of the low-energy physics, such as the spectrum of light particles at strong
coupling and the associated BPS quivers. We also study the gravitational
couplings on the $U$-plane, matching the infrared expectation for the couplings
$A(U)$ and $B(U)$ to the UV computation using the Nekrasov partition function.


Abstract:
  Standard methods for video recognition use large CNNs designed to capture
spatio-temporal data. However, training these models requires a large amount of
labeled training data, containing a wide variety of actions, scenes, settings
and camera viewpoints. In this paper, we show that current convolutional neural
network models are unable to recognize actions from camera viewpoints not
present in their training data (i.e., unseen view action recognition). To
address this, we develop approaches based on 3D representations and introduce a
new geometric convolutional layer that can learn viewpoint invariant
representations. Further, we introduce a new, challenging dataset for unseen
view recognition and show the approaches ability to learn viewpoint invariant
representations.


Abstract:
  For superconducting nanowire with the pairing of extended s-type symmetry,
Rashba spin-orbit interaction in a magnetic field, the influence of strong
intersite charge correlations on single-particle Majorana excitations is
analyzed. This problem is investigated on the basis of the density matrix
renormalization group numerical method. It is shown that with an increase in
the repulsion intensity of electrons located at the neighboring sites, two
subbands emerge in the lower Hubbard band of the open system. Based on
calculations of the Majorana polarization and degeneracy of the entanglement
spectrum, it was found that a topologically nontrivial phase with one edge
state survives at the edge of each of the subbands where the concentration of
electrons or holes is minimal.