Abstract:
  Traditional learning approaches for classification implicitly assume that
each mistake has the same cost. In many real-world problems though, the utility
of a decision depends on the underlying context $x$ and decision $y$. However,
directly incorporating these utilities into the learning objective is often
infeasible since these can be quite complex and difficult for humans to
specify.
  We formally study this as agnostic learning with unknown utilities: given a
dataset $S = \{x_1, \ldots, x_n\}$ where each data point $x_i \sim
\mathcal{D}$, the objective of the learner is to output a function $f$ in some
class of decision functions $\mathcal{F}$ with small excess risk. This risk
measures the performance of the output predictor $f$ with respect to the best
predictor in the class $\mathcal{F}$ on the unknown underlying utility $u^*$.
This utility $u^*$ is not assumed to have any specific structure. This raises
an interesting question whether learning is even possible in our setup, given
that obtaining a generalizable estimate of utility $u^*$ might not be possible
from finitely many samples. Surprisingly, we show that estimating the utilities
of only the sampled points~$S$ suffices to learn a decision function which
generalizes well.
  We study mechanisms for eliciting information which allow a learner to
estimate the utilities $u^*$ on the set $S$. We introduce a family of
elicitation mechanisms by generalizing comparisons, called the $k$-comparison
oracle, which enables the learner to ask for comparisons across $k$ different
inputs $x$ at once. We show that the excess risk in our agnostic learning
framework decreases at a rate of $O\left(\frac{1}{k} \right)$. This result
brings out an interesting accuracy-elicitation trade-off -- as the order $k$ of
the oracle increases, the comparative queries become harder to elicit from
humans but allow for more accurate learning.


Abstract:
  In this paper, we propose a fresh perspective on argumentation semantics, to
view them as a relational database. It offers encapsulation of the underlying
argumentation graph, and allows us to understand argumentation semantics under
a single, relational perspective, leading to the concept of relational
argumentation semantics. This is a direction to understand argumentation
semantics through a common formal language. We show that many existing
semantics such as explanation semantics, multi-agent semantics, and more
typical semantics, that have been proposed for specific purposes, are
understood in the relational perspective.


Abstract:
  Reputation is a powerful mechanism to enforce cooperation among unrelated
individuals through indirect reciprocity, but it suffers from disagreement
originating from private assessment, noise, and incomplete information. In this
work, we investigate stability of cooperation in the donation game by regarding
each player's reputation and behaviour as continuous variables. Through
perturbative calculation, we derive a condition that a social norm should
satisfy to give penalties to its close variants, provided that everyone
initially cooperates with a good reputation, and this result is supported by
numerical simulation. A crucial factor of the condition is whether a
well-reputed player's donation to an ill-reputed co-player is appreciated by
other members of the society, and the condition can be reduced to a threshold
for the benefit-cost ratio of cooperation which depends on the reputational
sensitivity to a donor's behaviour as well as on the behavioural sensitivity to
a recipient's reputation. Our continuum formulation suggests how indirect
reciprocity can work beyond the dichotomy between good and bad even in the
presence of inhomogeneity, noise, and incomplete information.


Abstract:
  The recently released spacecraft potential measured by the RPW instrument
on-board Solar Orbiter has been used to estimate the solar wind electron
density in the inner heliosphere. Solar-wind electron density measured during
June 2020 has been analysed to obtain a thorough characterization of the
turbulence and intermittency properties of the fluctuations. Magnetic field
data have been used to describe the presence of ion-scale waves. Selected
intervals have been extracted to study and quantify the properties of
turbulence. The Empirical Mode Decomposition has been used to obtain the
generalized marginal Hilbert spectrum, equivalent to the structure functions
analysis, and additionally reducing issues typical of non-stationary, short
time series. The presence of waves was quantitatively determined introducing a
parameter describing the time-dependent, frequency-filtered wave power. A well
defined inertial range with power-law scaling has been found almost everywhere.
However, the Kolmogorov scaling and the typical intermittency effects are only
present in part of the samples. Other intervals have shallower spectra and more
irregular intermittency, not described by models of turbulence. These are
observed predominantly during intervals of enhanced ion frequency wave
activity. Comparisons with compressible magnetic field intermittency (from the
MAG instrument) and with an estimate of the solar wind velocity (using electric
and magnetic field) are also provided to give general context and help
determine the cause for the anomalous fluctuations.


Abstract:
  Next-generation neutrinoless double beta decay experiments aim for half-life
sensitivities of ~$10^{27}$ yr, requiring suppressing backgrounds to <1
count/tonne/yr. For this, any extra background rejection handle, beyond
excellent energy resolution and the use of extremely radiopure materials, is of
utmost importance. The NEXT experiment exploits differences in the spatial
ionization patterns of double beta decay and single-electron events to
discriminate signal from background. While the former display two Bragg peak
dense ionization regions at the opposite ends of the track, the latter
typically have only one such feature. Thus, comparing the energies at the track
extremes provides an additional rejection tool. The unique combination of the
topology-based background discrimination and excellent energy resolution (1%
FWHM at the Q-value of the decay) is the distinguishing feature of NEXT.
Previous studies demonstrated a topological background rejection factor of ~5
when reconstructing electron-positron pairs in the $^{208}$Tl 1.6 MeV double
escape peak (with Compton events as background), recorded in the NEXT-White
demonstrator at the Laboratorio Subterr\'aneo de Canfranc, with 72% signal
efficiency. This was recently improved through the use of a deep convolutional
neural network to yield a background rejection factor of ~10 with 65% signal
efficiency. Here, we present a new reconstruction method, based on the
Richardson-Lucy deconvolution algorithm, which allows reversing the blurring
induced by electron diffusion and electroluminescence light production in the
NEXT TPC. The new method yields highly refined 3D images of reconstructed
events, and, as a result, significantly improves the topological background
discrimination. When applied to real-data 1.6 MeV $e^-e^+$ pairs, it leads to a
background rejection factor of 27 at 57% signal efficiency.


Abstract:
  Histopathology image embedding is an active research area in computer vision.
Most of the embedding models exclusively concentrate on a specific
magnification level. However, a useful task in histopathology embedding is to
train an embedding space regardless of the magnification level. Two main
approaches for tackling this goal are domain adaptation and domain
generalization, where the target magnification levels may or may not be
introduced to the model in training, respectively. Although magnification
adaptation is a well-studied topic in the literature, this paper, to the best
of our knowledge, is the first work on magnification generalization for
histopathology image embedding. We use an episodic trainable domain
generalization technique for magnification generalization, namely Model
Agnostic Learning of Semantic Features (MASF), which works based on the Model
Agnostic Meta-Learning (MAML) concept. Our experimental results on a breast
cancer histopathology dataset with four different magnification levels show the
proposed method's effectiveness for magnification generalization.


Abstract:
  Training coreference resolution models require comprehensively labeled data.
A model trained on one dataset may not successfully transfer to new domains.
This paper investigates an approach to active learning for coreference
resolution that feeds discrete annotations to an incremental clustering model.
The recent developments in incremental coreference resolution allow for a novel
approach to active learning in this setting. Through this new framework, we
analyze important factors in data acquisition, like sources of model
uncertainty and balancing reading and labeling costs. We explore different
settings through simulated labeling with gold data. By lowering the data
barrier for coreference, coreference resolvers can rapidly adapt to a series of
previously unconsidered domains.


Abstract:
  We model the Neupert effect that relates flare heating energy with the
observed SXR emission. The traditional form of the Neupert effect refers to the
correlation between the time-integrated HXR or microwave light curve and the
SXR light curve. In this paper, instead, we use as the proxy for heating energy
the ultraviolet (UV) emission at the foot-points of flare loops, and modify the
model of the Neupert effect by taking into account the discrete nature of flare
heating as well as cooling. In the modified empirical model, spatially resolved
UV lightcurves from the transition region or upper chromosphere are each
convolved with a kernel function characterizing the decay of the flare loop
emission. Contributions by all loops are summed to compare with the observed
total SXR emission. The model has successfully reproduced the observed SXR
emission from its rise to decay. To estimate heating energies in flare loops,
we also employ the UV Foot-point Calorimeter (UFC) method that infers heating
rates in flare loops from these UV light curves and models evolution of flare
loops with a zero-dimensional hydrodynamic code. The experiments show that a
multitude of impulsive heating events do not well reproduce the observed flare
SXR light curve, but a two-phase heating model leads to better agreement with
observations. Comparison of the two models of the Neupert effect further allows
us to calibrate the UFC method, and improve the estimate of heating rates in
flare loops continuously formed by magnetic reconnection throughout the flare
evolution.


Abstract:
  Motivated by cutting-edge applications like cryo-electron microscopy
(cryo-EM), the Multi-Reference Alignment (MRA) model entails the learning of an
unknown signal from repeated measurements of its images under the latent action
of a group of isometries and additive noise of magnitude $\sigma$. Despite
significant interest, a clear picture for understanding rates of estimation in
this model has emerged only recently, particularly in the high-noise regime
$\sigma \gg 1$ that is highly relevant in applications. Recent investigations
have revealed a remarkable asymptotic sample complexity of order $\sigma^6$ for
certain signals whose Fourier transforms have full support, in stark contrast
to the traditional $\sigma^2$ that arise in regular models. Often prohibitively
large in practice, these results have prompted the investigation of variations
around the MRA model where better sample complexity may be achieved. In this
paper, we show that \emph{sparse} signals exhibit an intermediate $\sigma^4$
sample complexity even in the classical MRA model. Our results explore and
exploit connections of the MRA estimation problem with two classical topics in
applied mathematics: the \textit{beltway problem} from combinatorial
optimization, and \textit{uniform uncertainty principles} from harmonic
analysis.


Abstract:
  Generalizing a result of \cite{Z1991, CPZ} about elliptic modular forms, we
give a closed formula for the sum of all Hilbert Hecke eigenforms over a
totally real number field with strict class number $1$, multiplied by their
period polynomials, as a single product of the Kronecker series.


Abstract:
  Inference on the extremal behaviour of spatial aggregates of precipitation is
important for quantifying river flood risk. There are two classes of previous
approach, with one failing to ensure self-consistency in inference across
different regions of aggregation and the other imposing highly restrictive
assumptions. To overcome these issues, we propose a model for high-resolution
precipitation data, from which we can simulate realistic fields and explore the
behaviour of spatial aggregates. Recent developments have seen spatial
extensions of the Heffernan and Tawn (2004) model for conditional multivariate
extremes, which can handle a wide range of dependence structures. Our
contribution is twofold: extensions and improvements of this approach and its
model inference for high-dimensional data; and a novel framework for deriving
aggregates addressing edge effects and sub-regions without rain. We apply our
modelling approach to gridded East-Anglia, UK precipitation data. Return-level
curves for spatial aggregates over different regions of various sizes are
estimated and shown to fit very well to the data.


Abstract:
  Hateful memes pose a unique challenge for current machine learning systems
because their message is derived from both text- and visual-modalities. To this
effect, Facebook released the Hateful Memes Challenge, a dataset of memes with
pre-extracted text captions, but it is unclear whether these synthetic examples
generalize to `memes in the wild'. In this paper, we collect hateful and
non-hateful memes from Pinterest to evaluate out-of-sample performance on
models pre-trained on the Facebook dataset. We find that memes in the wild
differ in two key aspects: 1) Captions must be extracted via OCR, injecting
noise and diminishing performance of multimodal models, and 2) Memes are more
diverse than `traditional memes', including screenshots of conversations or
text on a plain background. This paper thus serves as a reality check for the
current benchmark of hateful meme detection and its applicability for detecting
real world hate.


Abstract:
  Activists, journalists, and scholars have long raised critical questions
about the relationship between diversity, representation, and structural
exclusions in data-intensive tools and services. We build on work mapping the
emergent landscape of corporate AI ethics to center one outcome of these
conversations: the incorporation of diversity and inclusion in corporate AI
ethics activities. Using interpretive document analysis and analytic tools from
the values in design field, we examine how diversity and inclusion work is
articulated in public-facing AI ethics documentation produced by three
companies that create application and services layer AI infrastructure: Google,
Microsoft, and Salesforce.
  We find that as these documents make diversity and inclusion more tractable
to engineers and technical clients, they reveal a drift away from civil rights
justifications that resonates with the managerialization of diversity by
corporations in the mid-1980s. The focus on technical artifacts, such as
diverse and inclusive datasets, and the replacement of equity with fairness
make ethical work more actionable for everyday practitioners. Yet, they appear
divorced from broader DEI initiatives and other subject matter experts that
could provide needed context to nuanced decisions around how to operationalize
these values. Finally, diversity and inclusion, as configured by engineering
logic, positions firms not as ethics owners but as ethics allocators; while
these companies claim expertise on AI ethics, the responsibility of defining
who diversity and inclusion are meant to protect and where it is relevant is
pushed downstream to their customers.


Abstract:
  On page 10 of the 2018 National Academies Exoplanet Science Strategy document
(NASEM 2018), 'Expect the unexpected' is described as a general principle of
the exoplanet field. But for the next 150 pages, this principle is apparently
forgotten, as strategy decisions are repeatedly put forward based on our
expectations. This paper explores what exactly it might mean to 'expect the
unexpected', and how this could possibly be achieved by the space science
community. An analogy with financial investment strategies is considered, where
a balanced portfolio of low/medium/high-risk investments is recommended. Whilst
this kind of strategy would certainly be advisable in many scientific contexts
(past and present), in certain contexts, especially exploratory science, a
significant disanalogy needs to be factored in: financial investors cannot
choose low-risk high-reward investments, but sometimes scientists can. The
existence of low-risk high-impact projects in cutting-edge space science
significantly reduces the warrant for investing in high-risk projects, at least
in the short term. However, high-risk proposals need to be fairly judged
alongside medium- and low-risk proposals, factoring in both the degree of
possible reward and the expected cost of the project. Attitudes towards
high-risk high-impact projects within NASA since 2009 are critically analysed.


Abstract:
  Recently, the so-called relative transverse activity classifier, $R_{\rm T}$,
has been proposed as a tool to disentangle the particle production originated
from the soft and hard QCD processes in proton-proton (pp) collisions. $R_{\rm
T}$ is a useful quantity to study particle production in events with
exceptionally large or small activity in the transverse region with respect to
the event-averaged mean. Contrary to the expectations, the preliminary results
of the ALICE Collaboration indicate that, e.g., the proton-to-pion ratio does
not exhibit the characteristic enhancement at intermediate $p_{\rm T}$ in
events with large $R_{\rm T}$ with respect to minimum-bias pp collisions. In
this work, we investigate the origin of this effect using the PYTHIA 8 and
HERWIG 7 Monte Carlo event generators. The effect is a consequence of a
selection bias attributed to wide-angle gluon emissions which creates jets that
populate the transverse region. Therefore, we propose a modified version of
$R_{\rm T}$ in order to suppress its sensitivity to hard gluon Bremsstrahlung,
and enhance the sensitivity to soft Multiparton Interactions (MPI). This
approach could be useful in order to study the particle production in the
jet-like region as a function of MPI. The implementation of these ideas in data
will provide more insight into the production mechanisms of hadrons in
high-multiplicity pp collisions, and its connection with heavy-ion phenomena.


Abstract:
  We show that axions interacting with abelian gauge fields obtain a potential
from loops of magnetic monopoles. This is a consequence of the Witten effect:
the axion field causes the monopoles to acquire an electric charge and alters
their energy spectrum. The axion potential can also be understood as a type of
instanton effect due to a Euclidean monopole worldline winding around its dyon
collective coordinate. We calculate this effect, which has features in common
with both nonabelian instantons and Euclidean brane instantons. To provide
consistency checks, we argue that this axion potential vanishes in the presence
of a massless charged fermion and that it is robust against the presence of
higher-derivative corrections in the effective Lagrangian. Finally, as a first
step toward connecting with particle phenomenology and cosmology, we discuss
the regime in which this potential is important in determining the dark matter
relic abundance in a hidden sector containing an abelian gauge group,
monopoles, and axions.


Abstract:
  Permalloy films with one-dimensional (1D) profile modulation of submicron
periodicity are fabricated based on commercially available DVD-R discs and
studied using ferromagnetic resonance (FMR) method and micromagnetic numerical
simulations. The main resonance position shows in-plane angular dependence
which is strongly reminiscent of that in ferromagnetic films with uniaxial
magnetic anisotropy. The main signal and additional low field lines are
attributed to multiple standing spin wave resonances defined by the grating
period. The results may present interest in magnetic metamaterials and
magnonics applications.


Abstract:
  The ability to communicate intention enables decentralized multi-agent robots
to collaborate while performing physical tasks. In this work, we present
spatial intention maps, a new intention representation for multi-agent
vision-based deep reinforcement learning that improves coordination between
decentralized mobile manipulators. In this representation, each agent's
intention is provided to other agents, and rendered into an overhead 2D map
aligned with visual observations. This synergizes with the recently proposed
spatial action maps framework, in which state and action representations are
spatially aligned, providing inductive biases that encourage emergent
cooperative behaviors requiring spatial coordination, such as passing objects
to each other or avoiding collisions. Experiments across a variety of
multi-agent environments, including heterogeneous robot teams with different
abilities (lifting, pushing, or throwing), show that incorporating spatial
intention maps improves performance for different mobile manipulation tasks
while significantly enhancing cooperative behaviors.


Abstract:
  Intentional breaking of the lattice symmetry in solids is a key concept to
alter the properties of materials by modifying their electronic band structure.
However, the correlation of strain-induced effects and breaking of the lattice
symmetry is often indirect, resorting to vibrational spectroscopic techniques
such as Raman scattering. Here, we demonstrate that reflectance anisotropy
spectroscopy (RAS), which directly depends on the complex dielectric function,
enables the direct observation of electronic band structure modulation.
Studying the strain-induced symmetry breaking in copper, we show how uniaxial
strain lifts the degeneracy of states in the proximity of the both L and X
symmetry points, thus altering the matrix element for interband optical
transitions, directly observable in RAS. We corroborate our experimental
results by analysing the strain-induced changes in the electronic structure
based on ab-initio density functional theory calculations. The versatility to
study breaking of the lattice symmetry by simple reflectance measurements opens
up the possibility to gain a direct insight on the band-structure of other
strain-engineered materials, such as graphene and two-dimensional (2D)
transition metal dichalcogenides (TMDCs).


Abstract:
  This paper studies numerical methods for the approximation of elliptic PDEs
with lognormal coefficients of the form $-{\rm div}(a\nabla u)=f$ where
$a=\exp(b)$ and $b$ is a Gaussian random field. The approximant of the solution
$u$ is an $n$-term polynomial expansion in the scalar Gaussian random variables
that parametrize $b$. We present a general convergence analysis of weighted
least-squares approximants for smooth and arbitrarily rough random field, using
a suitable random design, for which we prove optimality in the following sense:
their convergence rate matches exactly or closely the rate that has been
established in \cite{BCDM} for best $n$-term approximation by Hermite
polynomials, under the same minimial assumptions on the Gaussian random field.
This is in contrast with the current state of the art results for the
stochastic Galerkin method that suffers the lack of coercivity due to the
lognormal nature of the diffusion field. Numerical tests with $b$ as the
Brownian bridge confirm our theoretical findings.


Abstract:
  It's long been accepted that continuous integration (CI) in software
engineering increases the code quality of enterprise projects when adhered to
by it's practitioners. But is any of that effort to increase code quality and
velocity directed towards improving software accessibility accommodations? What
are the potential benefits quoted in literature? Does it fit with the modern
agile way that teams operate in most enterprises? This paper attempts to map
the current scene of the software engineering effort spent on improving
accessibility via continuous integration and it's hurdles to adoption as quoted
by researchers. We also try to explore steps that agile teams may take to train
members on how to implement accessibility testing and introduce key diagrams to
visualize processes to implement CI based accessibility testing procedures in
the software development lifecycle.


Abstract:
  Estimating position and orientation change of a mobile platform from two
consecutive point clouds provided by a high-resolution sensor is a key problem
in autonomous navigation. In particular, scan matching algorithms aim to find
the translation and rotation of the platform such that the two point clouds
coincide. The association of measurements in point cloud one with measurements
in point cloud two is a problem inherent to scan matching. Existing methods
perform non-probabilistic data association, i.e., they assume a single
association hypothesis. This leads to overconfident pose estimates and reduced
estimation accuracy in ambiguous environments. Our probabilistic scan matching
approach addresses this issue by considering all association hypotheses with
their respective likelihoods. We formulate a holistic Bayesian estimation
problem for both data association and pose estimation and present the
corresponding joint factor graph. Near-optimum maximum a posteriori (MAP)
estimates of the sensor pose are computed by performing iterative message
passing on the factor graph. Our numerical study shows performance improvements
compared to non-probabilistic scan matching methods that are based on the
normal distributions transform (NDT) and implicit moving least squares (IMLS).


Abstract:
  For a compact Riemannian locally symmetric space $\Gamma\backslash G/K$ of
arbitrary rank we determine the location of certain Ruelle-Taylor resonances
for the Weyl chamber action. We provide a Weyl-lower bound on an appropriate
counting function for the Ruelle-Taylor resonances and establish a spectral gap
which is uniform in $\Gamma$ if $G/K$ is irreducible of higher rank. This is
achieved by proving a quantum-classical correspondence, i.e. a
1:1-correspondence between horocyclically invariant Ruelle-Taylor resonant
states and joint eigenfunctions of the algebra of invariant differential
operators on $G/K$.


Abstract:
  3D porous scaffolds based on agarose and nanocrystalline apatite, two
structural components that act as a temporary mineralized extracellular matrix,
were prepared by the GELPOR3D method. This shaping technology allows the
introduction of thermally-labile molecules within the scaffolds during the
fabrication procedure. An angiogenic protein, Vascular Endothelial Growth
Factor, and an antibiotic, cephalexin, loaded in mesoporous silica
nanoparticles, were included to design multifunctional scaffolds for bone
reconstruction. The dual release of both molecules showed a pro-angiogenic
behaviour in chicken embryos grown ex ovo, while, at the same time providing an
antibiotic local concentration capable of inhibiting Staphylococcus aureus
bacterial growth. In this sense, different release patterns, monitored by
UV-spectroscopy, could be tailored as a function of the cephalexin loading
strategy. The scaffold surface was characterized by a high hydrophilicity, as
determined by contact angle measurements, that facilitated the adhesion and
proliferation of preosteoblastic cells.


Abstract:
  In this article we study the problem of training intelligent agents using
Reinforcement Learning for the purpose of game development. Unlike systems
built to replace human players and to achieve super-human performance, our
agents aim to produce meaningful interactions with the player, and at the same
time demonstrate behavioral traits as desired by game designers. We show how to
combine distinct behavioral policies to obtain a meaningful "fusion" policy
which comprises all these behaviors. To this end, we propose four different
policy fusion methods for combining pre-trained policies. We further
demonstrate how these methods can be used in combination with Inverse
Reinforcement Learning in order to create intelligent agents with specific
behavioral styles as chosen by game designers, without having to define many
and possibly poorly-designed reward functions. Experiments on two different
environments indicate that entropy-weighted policy fusion significantly
outperforms all others. We provide several practical examples and use-cases for
how these methods are indeed useful for video game production and designers.


Abstract:
  We construct relativistic quantum Markov semigroups from covariant completely
positive maps. We proceed by generalizing a step in Stinespring's dilation to a
general system of imprimitivity and basing it on Poincar\'e group. The
resulting noise channels are relativistically consistent and the method is
applicable to any fundamental particle, though we demonstrate it for the case
of light-like particles. The Krauss decomposition of the relativistically
consistent completely positive identity preserving maps (our set up is in
Heisenberg picture) enables us to construct the covariant quantum Markov
semigroups that are uniformly continuous. We induce representations from the
little groups to ensure the quantum Markov semigroups that are ergodic due to
transitive systems imprimitivity.


Abstract:
  Business Model Canvas (BMC) is a tool widely used to describe startup
business models. Despite the various business aspects described, BMC pays a
little emphasis on team-related factors. The importance of team-related factors
in software development has been acknowledged widely in literature. While not
as extensively studied, the importance of teams in software startups is also
known in both literature and among practitioners. In this paper, we propose
potential changes to BMC to have the tool better reflect the importance of the
team, especially in a software startup environment. Based on a literature
review, we identify various components related to the team, which we then
further support with empirical data. We do so by means of a qualitative case
study of five startups.


Abstract:
  The 5$d$ based SrIrO$_3$ represents prototype example of nonmagnetic
correlated metal which mainly originates from a combined effect of spin-orbit
coupling, lattice dimensionality and crystal structure. Therefore, tuning of
these parameters results in diverse physical properties in this material. Here,
we study the structural, magnetic and electrical transport behavior in
epitaxial SrIrO$_3$ film ($\sim$ 40 nm) grown on SrTiO$_3$ substrate. Opposed
to bulk material, the SrIrO$_3$ film exhibits a ferromagnetic ordering at low
temperature below $\sim$ 20 K. The electrical transport data indicate an
insulating behavior where the nature of charge transport follows Mott's
variable-range-hopping model. A positive magnetoresistance is recorded at 2 K
which has correlation with magnetic moment. We further observe a nonlinear Hall
effect at low temperature ($<$ 20 K) which arises due to an anomalous component
of Hall effect. An anisotropic behavior of both magnetoresistance and Hall
effect has been evidenced at low temperature which coupled with anomalous Hall
effect indicate the development of ferromagnetic ordering. We believe that an
enhanced (local) structural distortion caused by lattice strain at low
temperatures induces ferromagnetic ordering, thus showing structural
instability plays vital role to tune the physical properties in SrIrO$_3$.


Abstract:
  There has been a steady need to precisely extract structured knowledge from
the web (i.e. HTML documents). Given a web page, extracting a structured object
along with various attributes of interest (e.g. price, publisher, author, and
genre for a book) can facilitate a variety of downstream applications such as
large-scale knowledge base construction, e-commerce product search, and
personalized recommendation. Considering each web page is rendered from an HTML
DOM tree, existing approaches formulate the problem as a DOM tree node tagging
task. However, they either rely on computationally expensive visual feature
engineering or are incapable of modeling the relationship among the tree nodes.
In this paper, we propose a novel transferable method, Simplified DOM Trees for
Attribute Extraction (SimpDOM), to tackle the problem by efficiently retrieving
useful context for each node by leveraging the tree structure. We study two
challenging experimental settings: (i) intra-vertical few-shot extraction, and
(ii) cross-vertical fewshot extraction with out-of-domain knowledge, to
evaluate our approach. Extensive experiments on the SWDE public dataset show
that SimpDOM outperforms the state-of-the-art (SOTA) method by 1.44% on the F1
score. We also find that utilizing knowledge from a different vertical
(cross-vertical extraction) is surprisingly useful and helps beat the SOTA by a
further 1.37%.


Abstract:
  Quantum annealing in the transverse-field Ising model (TFIM) with open-system
dynamics is known to use thermally-assisted tunneling to drive computation.
However, it is still subject to debate whether quantum systems in the presence
of decoherence are more useful than those using classical dynamics to drive
computation. We contribute to this debate by introducing the perturbed
ferromagnetic chain (PFC), a chain of frustrated sub-systems where the degree
of frustration scales inversely with the perturbation introduced by a tunable
parameter. This gives us an easily embeddable gadget whereby problem hardness
can be tuned for systems of constant size. We outline the properties of the PFC
and compare classical spin-vector Monte Carlo (SVMC) variants with the
adiabatic quantum master equation. We demonstrate that SVMC methods get trapped
in the exponentially large first-excited-state manifold when solving this
frustrated problem, whereas evolution using quantum dynamics remains in the
lowest energy eigenstates. This results in significant differences in ground
state probability when using either classical or quantum annealing dynamics in
the TFIM.


Abstract:
  Gross--Joyce--Tanaka arXiv:2005.05637 proposed a wall-crossing conjecture for
Calabi--Yau fourfolds. Assuming that it holds, we prove the conjecture of
Cao--Kool arXiv:1712.07347 for 0-dimensional DT4 invariants on projective
Calabi--Yau 4-folds and then compute virtual fundamental classes of Hilbert
schemes of points. As a results we are able to express generating series of
invariants in terms of universal power-series. On $\CC^4$, Nekrasov proposed
invariants with a conjectured closed form arXiv:1712.08128. Assuming
wall-crossing, we prove an analogue of his formula for compact CY 4-folds.
Finally, we notice a relationship to corresponding generating series for
Quot-schemes on elliptic surfaces which are also governed by a wall-crossing
formula. This leads to Segre--Verlinde correspondence for Calabi--Yau
fourfolds. We will study the relation further in the sequence to this paper.


Abstract:
  We theoretically study the orbital magnetic susceptibility produced by
electron states near a crossing point of two band-contact lines in a crystal.
It is shown that this susceptibility can have an unusual dependence on the
Fermi level and can change noticeably with the temperature when the Fermi level
is in the vicinity of the crossing point. These features of the magnetic
susceptibility can be useful in detecting the crossing points in crystal. The
obtained results explain the well-known temperature dependence of the magnetic
susceptibility of V$_3$Si.


Abstract:
  Recent formal classifications of crystalline topological insulators predict
that the combination of time-reversal and rotational symmetry gives rise to
topological invariants beyond the ones known for other lattice symmetries.
Although the classification proves their existence, it does not indicate a way
of calculating the values of those invariants. Here, we show that a specific
set of concentric Wilson loops and line invariants yields the values of all
topological invariants in two-dimensional systems with pure rotation symmetry
in class AII. Examples of this analysis are given for specific models with
two-fold and three-fold rotational symmetry. We find new invariants that relate
to the presence of higher-order topology and corner charges.


Abstract:
  Cosmological inference from cluster number counts is systematically limited
by the accuracy of the mass calibration, i.e. the empirical determination of
the mapping between cluster selection observables and halo mass. In this work
we demonstrate a method to quantitatively determine the bias and uncertainties
in weak-lensing mass calibration. To this end, we extract a library of
projected matter density profiles from hydrodynamical simulations. Accounting
for shear bias and noise, photometric redshift uncertainties, mis-centering,
cluster member contamination, cluster morphological diversity, and
line-of-sight projections, we produce a library of shear profiles. Fitting a
one-parameter model to these profiles, we extract the so-called \emph{weak
lensing mass} $M_\text{WL}$. Relating the weak-lensing mass to the halo mass
from gravity-only simulations with the same initial conditions as the
hydrodynamical simulations allows us to estimate the impact of hydrodynamical
effects on cluster number counts experiments. Creating new shear libraries for
$\sim$1000 different realizations of the systematics, provides a distribution
of the parameters of the weak-lensing to halo mass relation, reflecting their
systematic uncertainty. This result can be used as a prior for cosmological
inference. We also discuss the impact of the inner fitting radius on the
accuracy, and determine the outer fitting radius necessary to exclude the
signal from neighboring structures. Our method is currently being applied to
different Stage~III lensing surveys, and can easily be extended to Stage~IV
lensing surveys.


Abstract:
  Machine learning approaches in drug discovery, as well as in other areas of
the chemical sciences, benefit from curated datasets of physical molecular
properties. However, there is a lack of sufficiently large data collections
that include first-principle quantum chemical information on bioactive
molecules, such as single-point electronic properties, quantum mechanical wave
functions and density-functional theory (DFT) matrices. The open-access QMugs
(Quantum-Mechanical Properties of Drug-like Molecules) dataset fills this void.
The QMugs collection comprises quantum mechanical properties of more than 665k
biologically and pharmacologically relevant molecules extracted from the ChEMBL
database, totaling $\sim$2M conformers. QMugs contains optimized molecular
geometries and thermodynamic data obtained via the semi-empirical method
GFN2-xTB. Atomic and molecular properties (e.g., partial charges, energies, and
rotational constants) are provided on both the GFN2-xTB and on the DFT
($\omega$B97X-D/def2-SVP) levels of theory. QMugs also comprises the respective
quantum mechanical wave functions, including DFT density and orbital matrices,
totaling over 7 terabytes of uncompressed data. This dataset is intended to
facilitate the development of models that learn from molecular data on
different levels of theory while also providing insight into the corresponding
relationships between molecular structure and biological activity.


Abstract:
  For two unknown quantum states $\rho$ and $\sigma$ in an $N$-dimensional
Hilbert space, computing their fidelity $F(\rho,\sigma)$ is a basic problem
with many important applications in quantum computing and quantum information,
for example verification and characterization of the outputs of a quantum
computer, and design and analysis of quantum algorithms. In this Letter, we
propose a quantum algorithm that solves this problem in $\text{poly}(\log (N),
r)$ time, where $r$ is the lower rank of $\rho$ and $\sigma$. This algorithm
exhibits an exponential improvement over the best-known algorithm (based on
quantum state tomography) in $\text{poly}(N, r)$ time.


Abstract:
  Given a cusp form $f$ which is supersingular at a fixed prime $p$ away from
the level, and a Coleman family $F$ through one of its $p$-stabilisations, we
construct a $2$-variable meromorphic $p$-adic $L$-function for the symmetric
square of $F$, denoted $L^{\mathrm{imp}}_p(\mathrm{Sym}^2 F)$. We prove that
this new $p$-adic $L$-function interpolates values of complex imprimitive
symmetric square $L$-functions, for the various specialisations of the family
$F$. It is in fact uniquely determined by its interpolation properties. We also
prove that the function $L^{\mathrm{imp}}_p(\mathrm{Sym}^2 F)$ satisfies a
functional equation. We use this $p$-adic $L$-function to prove a $p$-adic
factorisation formula, expressing the geometric $p$-adic $L$-function attached
to the self-convolution of $F$, as the product of
$L^{\mathrm{imp}}_p(\mathrm{Sym}^2 F)$ and a Kubota-Leopoldt $L$-function. This
extends a result of Dasgupta in the ordinary case.
  Using Beilinson-Flach classes constructed by Kings, Zerbes and the second
author we construct motivic cohomology classes $b_f$, and prove that, under
some hypotheses, they differ by a scalar factor from the higher cyclotomic
classes constructed by Beilinson. Using this relation, we prove the
interpolation formulae for $L^{\mathrm{imp}}_p(\mathrm{Sym}^2 F)$ and the
factorisation formula.


Abstract:
  The BESIII Collaboration has observed a candidate for a $c \bar c s \bar u$
tetraquark $Z_{cs}$ at $(3982.5^{+1.8}_{-2.6} \pm 2.1)$ MeV and width
$(12.8^{+5.3}_{-4.4} \pm 3.0)$ MeV, while the LHCb Collaboration has observed a
$Z_{cs}$ candidate in the $\jpsi K^-$ channel with mass of $(4003 \pm 6
^{+4}_{-14})$ MeV and width $(131 \pm 15 \pm 26)$ MeV. In this note we examine
the possibility that these two states are distinct eigenstates of a mixing
process similar to that which gives rise to two axial-vector mesons labeled by
the Particle Data Group $K_1(1270)$ and $K_1(1400)$. The main point is that on
top of a $\bar c c$ pair, the $Z_{cs}$ states have the same light quark content
as the $K_1$-s. In the compact tetraquark picture this implies several
additional states, analogous to members of the $K_1$ nonet. These states have
not yet been observed, nor are they required in the molecular approach. Thus
experimental discovery or exclusion of these extra states will be a critical
test for competing models of exotic mesons with hidden charm.


Abstract:
  Packet transmission scheduling on multi-hop wireless sensor networks with
3-egress gateway linear topology is studied. Each node generates a data packet
in every one cycle period and forwards it bounded for either of gateways at
edges. We focus on centrally-managed Time Division Multiple Access (TDMA)-based
slot allocations and provide the design of a packet transmission scheduling
framework with static time-slot allocation and basic redundant transmission to
reduce and recover packet losses. We proposed three general path models, which
would represent all possible variations of this topology, and derived a global
static time-slot allocation on each of models to maximize the theoretical
probability that all packets are successfully delivered to one of the gateways
within one cycle period.


Abstract:
  Machine learning algorithms with empirical risk minimization usually suffer
from poor generalization performance due to the greedy exploitation of
correlations among the training data, which are not stable under distributional
shifts. Recently, some invariant learning methods for out-of-distribution (OOD)
generalization have been proposed by leveraging multiple training environments
to find invariant relationships. However, modern datasets are frequently
assembled by merging data from multiple sources without explicit source labels.
The resultant unobserved heterogeneity renders many invariant learning methods
inapplicable. In this paper, we propose Heterogeneous Risk Minimization (HRM)
framework to achieve joint learning of latent heterogeneity among the data and
invariant relationship, which leads to stable prediction despite distributional
shifts. We theoretically characterize the roles of the environment labels in
invariant learning and justify our newly proposed HRM framework. Extensive
experimental results validate the effectiveness of our HRM framework.


Abstract:
  We analyze Lindblad-Gorini-Kossakowski-Sudarshan-type generators for selected
periodically driven open quantum systems. All these generators can be obtained
by temporal coarse-graining procedures, and we compare different
coarse-graining schemes. Similar as for undriven systems, we find that a
dynamically adapted coarse-graining time, effectively yielding non-Markovian
dynamics by interpolating through a series of different but invididually
Markovian solutions, gives the best results among the different coarse-graining
schemes, albeit at highest computational cost.


Abstract:
  The objective of this study is to examine spatial patterns of impacts and
recovery of communities based on variances in credit card transactions. Such
variances could capture the collective effects of household impacts, disrupted
accesses, and business closures, and thus provide an integrative measure for
examining disaster impacts and community recovery in disasters. Existing
studies depend mainly on survey and sociodemographic data for disaster impacts
and recovery effort evaluations, although such data has limitations, including
large data collection efforts and delayed timeliness results. In addition,
there are very few studies have concentrated on spatial patterns and
disparities of disaster impacts and short-term recovery of communities,
although such investigation can enhance situational awareness during disasters
and support the identification of disparate spatial patterns of disaster
impacts and recovery in the impacted regions. This study examines credit card
transaction data Harris County (Texas, USA) during Hurricane Harvey in 2017 to
explore spatial patterns of disaster impacts and recovery during from the
perspective of community residents and businesses at ZIP code and county
scales, respectively, and to further investigate their spatial disparities
across ZIP codes. The results indicate that individuals in ZIP codes with
populations of higher income experienced more severe disaster impact and
recovered more quickly than those located in lower-income ZIP codes for most
business sectors. Our findings not only enhance the understanding of spatial
patterns and disparities in disaster impacts and recovery for better community
resilience assessment, but also could benefit emergency managers, city
planners, and public officials in harnessing population activity data, using
credit card transactions as a proxy for activity, to improve situational
awareness and resource allocation.


Abstract:
  This paper investigates problems associated with the valuation of callable
American volatility put options. Our approach involves modeling volatility
dynamics as a mean-reverting 3/2 volatility process. We first propose a pricing
formula for the perpetual American knock-out put. Under the given conditions,
the value of perpetual callable American volatility put options is discussed.


Abstract:
  Natural selection drives species to develop brains, with sizes that increase
with the complexity of the tasks to be tackled. Our goal is to investigate the
balance between the metabolic costs of larger brains compared to the advantage
they provide in solving general and combinatorial problems. Defining advantage
as the performance relative to competitors, a two-player game based on the
knapsack problem is used. Within this framework, two opponents compete over
shared resources, with the goal of collecting more resources than the opponent.
Neural nets of varying sizes are trained using a variant of the AlphaGo Zero
algorithm. A surprisingly simple relation, $N_A/(N_A+N_B)$, is found for the
relative win rate of a net with $N_A$ neurons against one with $N_B$. Success
increases linearly with investments in additional resources when the networks
sizes are very different, i.e. when $N_A \ll N_B$, with returns diminishing
when both networks become comparable in size.


Abstract:
  Quantum phase transitions (QPTs) are usually associated with many-body
systems with large degrees of freedom approaching the thermodynamic limit. In
such systems, the many-body ground state shows abrupt changes at zero
temperature when the control parameter of the Hamiltonian is scanned across a
quantum critical point. Recently it has been realized that a QPT can also occur
in a simple system composed of only a two-level atom and a single-mode bosonic
field, described by the quantum Rabi model (QRM). Here we report the first
experimental demonstration of a QPT in the QRM using a single trapped ion. We
measure the average spin-up state population of the ion and the average phonon
number in its spatial oscillation mode as two order parameters and observe the
clear evidences of the phase transition via slow quench of the coupling between
the ion and its spatial motion. An experimental probe of the phase transitions
in a fundamental quantum optics model without imposing the thermodynamic limit
opens up a new window for the controlled study of QPTs and quantum critical
phenomena.


Abstract:
  We investigate the fate of coherence in the dynamical evolution of a symmetry
protected quantum system. Under the formalism of system-plus-bath for open
quantum system, the anti-unitary symmetry exhibits significant difference from
the unitary one in protecting initial coherence. Specifically, taking advantage
of Lindblad master equation, we find that a pure state in the symmetry
protected degenerate subspace will decohere even though both the system
Hamiltonian and system-environment interaction respect the same anti-unitary
symmetry. In contrast, the coherence will persist when the protecting symmetry
is unitary. We provide an elaborate classification table to illustrate what
kinds of symmetry combinations are able to preserve the coherence of initial
state, which is confirmed by several concrete models in spin-$3/2$ system. Our
results could help to explore the possible experimental realization of stable
time-reversal symmetric topological states.


Abstract:
  Motility is a ubiquitous feature of microbial life on Earth, and is widely
regarded as a promising biosignature candidate. In the search for motile
organisms, it is therefore valuable to have rough estimates for the number of
such microbes that one may expect to find in a given area or volume. In this
work, we explore this question by employing a simple theoretical model that
takes into account the amount of free energy available in a given environment
and the energetic cost of motility. We present heuristic upper bounds for the
average biomass density and the number density of motile lifeforms for the
Martian subsurface and the ocean of Enceladus by presuming that the motile
microbes in question derive their energy from methanogenesis. We consequently
demonstrate that the resultant densities of motile organisms might be
potentially comparable to, or much lower than, the total microbial densities
documented in various extreme environments on Earth.


Abstract:
  The dynamics of polarized fluorescence in reduced nicotinamide adenine
dinucleotide (NADH) at 436~nm under two-photon excitation at 720~nm by
femtosecond laser pulses in alcohol dehydrogenase (ADH)-containing buffer
solution has been studied experimentally and theoretically. A global fit
procedure was used for determination of the fluorescence parameters from
experimental data. The interpretation of the experimental results obtained was
supported by \emph{ab initio} calculations of NADH structure in solutions. A
theoretical model was developed for description of the polarized fluorescence
decay in enzyme-NADH binary complexes that considered several possible
interaction scenarios. We suggest that the origin of a significant enhancement
of the nanosecond decay time value in the ADH-bounded NADH compare with the
free NADH can be attributed to the significant decrease of non-radiative
relaxation probabilities due to decrease of charges separations in the
nicotinamide ring in the conditions of an apolar ADH-NADH binding site
environment. The existence of a single decay time in the ADH-NADH complex in
the nanosecond time-domain in comparison with two decay times observed in free
NADH can be attributed to a single NADH unfolded \emph{trans}-like conformation
bounded within the ADH site. Comparison of the experimental data obtained and
the theory developed suggested the existence of an anisotropic relaxation time
of about 1 ns related most likely with interactions between excited NADH and
the ADH binding site that resulted in the rearrangement of nuclear distribution
and rotation of fluorescence transition dipole moment.


Abstract:
  Nitrogen-vacancy quantum defects in diamond offer a promising platform for
magnetometry because of their remarkable optical and spin properties. In this
Letter, we present a high-sensitivity and wide-bandwidth fiber-based quantum
magnetometer for practical applications. By coating the diamond surface with
silver reflective film, both the fluorescence collection and excitation
efficiency are enhanced. Additionally, tracking pulsed optically detected
magnetic resonance spectrum allowed a magnetic field sensitivity of $35$
pT$/\sqrt{\rm{Hz}}$ and a bandwidth of $4.1$ KHz. Finally, this magnetometer
was successfully applied to map the magnetic field induced by the
current-carrying copper-wire mesh. Such a stable and compact magnetometry can
provide a powerful tool in many areas of physical, chemical, and biological
researches.


Abstract:
  Recently, CycleGAN was shown to provide high-performance, ultra-fast
denoising for low-dose X-ray computed tomography (CT) without the need for a
paired training dataset. Although this was possible thanks to cycle
consistency, CycleGAN requires two generators and two discriminators to enforce
cycle consistency, demanding significant GPU resources and technical skills for
training. A recent proposal of tunable CycleGAN with Adaptive Instance
Normalization (AdaIN) alleviates the problem in part by using a single
generator. However, two discriminators and an additional AdaIN code generator
are still required for training. To solve this problem, here we present a novel
cycle-free Cycle-GAN architecture, which consists of a single generator and a
discriminator but still guarantees cycle consistency. The main innovation comes
from the observation that the use of an invertible generator automatically
fulfills the cycle consistency condition and eliminates the additional
discriminator in the CycleGAN formulation. To make the invertible generator
more effective, our network is implemented in the wavelet residual domain.
Extensive experiments using various levels of low-dose CT images confirm that
our method can significantly improve denoising performance using only 10% of
learnable parameters and faster training time compared to the conventional
CycleGAN.


Abstract:
  Let $(A,\sigma)$ be an Azumaya algebra with involution over a regular ring
$R$. We prove that the Gersten-Witt complex of $(A,\sigma)$ defined by Gille is
isomorphic to the Gersten-Witt complex of $(A,\sigma)$ defined by
Bayer-Fluckiger, Parimala and the author. Advantages of both constructions are
used to show that the Gersten-Witt complex is exact when $\dim R\leq 3$,
$\mathrm{ind}\, A\leq 2$ and $\sigma$ is orthogonal or symplectic. This means
that the Grothendieck-Serre conjecture holds for the group $R$-scheme of
$\sigma$-unitary elements in $A$ under the same hypotheses; $R$ is not required
to contain a field.


Abstract:
  The transfer matrix ${\mathbf{M}}$ of a short-range potential may be
expressed in terms of the time-evolution operator for an effective two-level
quantum system with a time-dependent non-Hermitian Hamiltonian. This leads to a
dynamical formulation of stationary scattering. We explore the utility of this
formulation in the study of the low-energy behavior of the scattering data. In
particular, for the exponentially decaying potentials, we devise a simple
iterative scheme for computing terms of arbitrary order in the series expansion
of ${\mathbf{M}}$ in powers of the wavenumber. The coefficients of this series
are determined in terms of a pair of solutions of the zero-energy stationary
Schr\"odinger equation. We introduce a transfer matrix for the latter equation,
express it in terms of the time-evolution operator for an effective two-level
quantum system, and use it to obtain a perturbative series expansion for the
solutions of the zero-energy stationary Schr\"odinger equation. Our approach
allows for identifying the zero-energy resonances for scattering potentials in
both full line and half-line with zeros of the entries of the zero-energy
transfer matrix of the potential or its trivial extension to the full line.


Abstract:
  The dynamical system described herein uses a hybrid cellular automata (CA)
mechanism to attain reversibility, and this approach is adapted to create a
novel block cipher algorithm called HCA. CA are widely used for modeling
complex systems and employ an inherently parallel model. Therefore,
applications derived from CA have a tendency to fit very well in the current
computational paradigm where scalability and multi-threading potential are
quite desirable characteristics. HCA model has recently received a patent by
the Brazilian agency INPI. Several evaluations and analyses performed on the
model are presented here, such as theoretical discussions related to its
reversibility and an analysis based on graph theory, which reduces HCA security
to the well-known Hamiltonian cycle problem that belongs to the NP-complete
class. Finally, the cryptographic robustness of HCA is empirically evaluated
through several tests, including avalanche property compliance and the NIST
randomness suite.


Abstract:
  We introduce a model of the diffusion of an epidemic with demographically
heterogeneous agents interacting socially on a spatially structured network.
Contagion-risk averse agents respond behaviorally to the diffusion of the
infections by limiting their social interactions. Firms also respond by
allowing employees to work remotely, depending on their productivity. The
spatial structure induces local herd immunities along socio-demographic
dimensions, which significantly affect the dynamics of infections. We study
several non-pharmaceutical interventions; e.g., i) lockdown rules, which set
thresholds on the spread of the infection for the closing and reopening of
economic activities; and ii) selective lockdowns, which restrict social
interactions by location (in the network) and by the demographic
characteristics of the agents. Substantiating a "Lucas critique" argument, we
assess the cost of naive discretionary policies ignoring agents and firms'
behavioral responses.


Abstract:
  Context: Securing microservice-based applications is crucial, as many IT
companies are delivering their businesses through microservices. If security
smells affect microservice-based applications, they can possibly suffer from
security leaks and need to be refactored to mitigate the effects of security
smells therein. Objective: As the currently available knowledge on securing
microservices is scattered across different pieces of white and grey
literature, our objective here is to distill well-known smells for securing
microservices, together with the refactorings enabling to mitigate the effects
of such smells. Method: To capture the state of the art and practice in
securing microservices, we conducted a multivocal review of the existing white
and grey literature on the topic. We systematically analyzed 58 studies
published from 2014 until the end of 2020. Results: Ten bad smells for securing
microservices are identified, which we organized in a taxonomy, associating
each smell with the security properties it may violate and the refactorings
enabling to mitigate its effects. Conclusions: The security smells and the
corresponding refactorings have pragmatic value for practitioners, who can
exploit them in their daily work on securing microservices. They also serve as
a starting point for researchers wishing to establish new research directions
on securing microservices.


Abstract:
  When quantum flavor Hall insulator phases of itinerant fermions are
disordered by strong quantum fluctuations, the condensation of skyrmion
textures of order parameter fields can lead to superconductivity. In this work,
we address the mechanism of skyrmion condensation by considering the scattering
between (2+1)-dimensional, Weyl fermions and hedgehog type tunneling
configurations of order parameters that violate the skyrmion-number
conservation law. We show the quantized, flavor Hall conductivity
($\sigma^f_{xy}$) controls the degeneracy of topologically protected, fermion
zero-modes, localized on hedgehogs, and the overlap between zero-mode
eigenfunctions or 't Hooft vertex determines the nature of pairing. We
demonstrate the quantum-disordered, flavor Hall insulators with $\sigma^f_{xy}=
2 N$ lead to different types of charge $2 N e^-$ superconductivity. Some
implications for the competition among flavor Hall insulators, the charge
$2e^-$ paired states in BCS and pair-density-wave channels, and the composite,
charge $4e^-$ superconductors for twisted bilayer graphene are outlined.


Abstract:
  We present Turnstile+, a high-level, macros-based metaDSL for building
dependently typed languages. With it, programmers may rapidly prototype and
iterate on the design of new dependently typed features and extensions. Or they
may create entirely new DSLs whose dependent type "power" is tailored to a
specific domain. Our framework's support of language-oriented programming also
makes it suitable for experimenting with systems of interacting components,
e.g., a proof assistant and its companion DSLs. This paper explains the
implementation details of Turnstile+, as well as how it may be used to create a
wide-variety of dependently typed languages, from a lightweight one with
indexed types, to a full spectrum proof assistant, complete with a tactic
system and extensions for features like sized types and SMT interaction.


Abstract:
  Deep equilibrium networks (DEQs) are a new class of models that eschews
traditional depth in favor of finding the fixed point of a single nonlinear
layer. These models have been shown to achieve performance competitive with the
state-of-the-art deep networks while using significantly less memory. Yet they
are also slower, brittle to architectural choices, and introduce potential
instability to the model. In this paper, we propose a regularization scheme for
DEQ models that explicitly regularizes the Jacobian of the fixed-point update
equations to stabilize the learning of equilibrium models. We show that this
regularization adds only minimal computational cost, significantly stabilizes
the fixed-point convergence in both forward and backward passes, and scales
well to high-dimensional, realistic domains (e.g., WikiText-103 language
modeling and ImageNet classification). Using this method, we demonstrate, for
the first time, an implicit-depth model that runs with approximately the same
speed and level of performance as popular conventional deep networks such as
ResNet-101, while still maintaining the constant memory footprint and
architectural simplicity of DEQs. Code is available at
https://github.com/locuslab/deq .


Abstract:
  To describe a spin-$\frac{1}{2}$ particle on the Bloch sphere with a radial
magnetic field and topological states of matter from the reciprocal space, we
introduce $C$ square ($C^2$) as a local formulation of the global topological
invariant. For the Haldane model on the honeycomb lattice, this $C^2$ can be
measured from the Dirac points through circularly polarized light related to
the high-symmetry $M$ point(s). For the quantum spin Hall effect and the
Kane-Mele model, the $\mathbb{Z}_2$ topological number robust to interactions
can be measured locally from a correspondence between the pfaffian and light.
We address a relation with a spin pump and the quantum spin Hall conductance.
The analogy between light and magnetic nuclear resonance may be applied for
imaging, among other applications.


Abstract:
  Enabling high data-rate uplink cellular connectivity for drones is a
challenging problem, since a flying drone has a higher likelihood of having
line-of-sight propagation to base stations that terrestrial UEs normally do not
have line-of-sight to. This may result in uplink inter-cell interference and
uplink performance degradation for the neighboring ground UEs when drones
transmit at high data-rates (e.g., video streaming). We address this problem
from a cellular operator's standpoint to support drone-sourced video streaming
of a point of interest. We propose a low-complexity, closed-loop control system
for Open-RAN architectures that jointly optimizes the drone's location in space
and its transmission directionality to support video streaming and minimize its
uplink interference impact on the network. We prototype and experimentally
evaluate the proposed control system on a dedicated outdoor multi-cell RAN
testbed, which is the first measurement campaign of its kind. Furthermore, we
perform a large-scale simulation assessment of the proposed control system
using the actual cell deployment topologies and cell load profiles of a major
US cellular carrier. The proposed Open-RAN control scheme achieves an average
19% network capacity gain over traditional BS-constrained control solutions and
satisfies the application data-rate requirements of the drone (e.g., to stream
an HD video).


Abstract:
  To precisely measure and evaluate X-ray generation and evolution in a
hohlraum during an implosion process, we present a two-dimensional (2D) time-
and space-resolved diagnostic method by combining a compressed ultrafast
photography (CUP) system and a simplified version of space-resolving flux
detector (SSRFD). Numerical experiment results showed that the reconstruction
quality of the conventional CUP significantly improved owing to the addition of
the external SSRFD, especially when a coded mask with a large pixel size was
used in the CUP. Further, the performance of the CUP cooperation with the SSRFD
was better than that of adding an external charge-coupled device or streak
camera. Compared with existing ultrafast imaging techniques in laser fusion,
the proposed method has a prominent advantage of measuring the 2D evolution of
implosion by combining high temporal resolution of streak camera and high
spatial resolution of SSRFD; moreover, it can provide guidance for designing
diagnostic experiments in laser fusion research.


Abstract:
  We consider a Lorentz violating scalar field cosmological model given by the
modified Einstein-aether theory defined in Weyl integrable geometry. The
existence of exact and analytic solutions is investigated for the case of a
spatially flat Friedmann--Lema\^{\i}tre--Robertson--Walker background space. We
show that the theory admits cosmological solutions of special interests. In
addition, we prove that the cosmological field equations admit the Lewis
invariant as a second conservation law, which indicates the integrability of
the field equations.


Abstract:
  Stroke poses an immense public health burden and remains among the primary
causes of death and disability worldwide. Emergent therapy is often precluded
by late or indeterminate times of onset before initial clinical presentation.
Rapid, mobile, safe and low-cost stroke detection technology remains a deeply
unmet clinical need. Past studies have explored the use of microwave and other
small form-factor strategies for rapid stroke detection; however, widespread
clinical adoption remains unrealized. Here, we investigated the use of
microwave scattering perturbations from ultra-wide-band antenna arrays to learn
dielectric signatures of disease. Two deep neural networks (DNNs) were used
for: 1) stroke detection ("classification network"), and 2) characterization of
the hemorrhage location and size ("discrimination network"). Dielectric
signatures were learned on a simulated cohort of 666 hemorrhagic stroke and
control subjects using 2D stochastic head models. The classification network
yielded a stratified K-fold stroke detection accuracy of 94.60% with standard
deviation of 2.86% and an AUC of 0.996, while the discrimination network
resulted in a mean squared error of <0.004 cm and <0.02 cm, for the stroke
localization and size estimation, respectively. We report a novel approach to
intelligent diagnostics using microwave wide-band scattering information thus
circumventing conventional image-formation.


Abstract:
  Theoretically, the helical edge states of two-dimensional topological
insulators are protected from coherent backscattering due to nonmagnetic
disorder provided electron interactions are not too strong. Experimentally, the
edges typically do not demonstrate the systematic and robust quantization, at
the same time little is known about the sub-Kelvin temperature behavior. Here,
we report the surprising localization of the edge states in an 8 nm HgTe
quantum well in zero magnetic field at millikelvin temperatures. Additionally,
the magnetoresistance data at 0.5 K for the edges few micrometers long suggests
the field-dependent localization length $l_B\propto B^{-\alpha}$, with $\alpha$
ranging approximately from $1.6$ to $2.8$ at fields $B\lesssim0.1\,\text{T}$
and $\alpha\approx1.1$ at higher fields up to $0.5\,\text{T}$. In the frame of
disordered interacting edge, these values of $\alpha$ correspond to the
Luttinger liquid parameters $K\approx 0.9-1.1$ and $K\approx 0.6$,
respectively. We discuss possible scenarios which could result in the zero
magnetic field localization.


Abstract:
  We establish a Wold-type decomposition for isometric and isometric
Nica-covariant representations of the odometer semigroup. These generalize the
Wold-type decomposition for commuting pairs of isometries due to Popovici and
for pairs of doubly commuting isometries due to S{\l}oci\'nski.


Abstract:
  Generative diffusion processes are an emerging and effective tool for image
and speech generation. In the existing methods, the underline noise
distribution of the diffusion process is Gaussian noise. However, fitting
distributions with more degrees of freedom, could help the performance of such
generative models. In this work, we investigate other types of noise
distribution for the diffusion process. Specifically, we show that noise from
Gamma distribution provides improved results for image and speech generation.
Moreover, we show that using a mixture of Gaussian noise variables in the
diffusion process improves the performance over a diffusion process that is
based on a single distribution. Our approach preserves the ability to
efficiently sample state in the training diffusion process while using Gamma
noise and a mixture of noise.


Abstract:
  Coherent production of $J/\psi$ mesons is studied in ultraperipheral
lead-lead collisions at a nucleon-nucleon centre-of-mass energy of 5 TeV, using
a data sample collected by the LHCb experiment corresponding to an integrated
luminosity of about $10 \mu b^{-1}$. The $J/\psi$ mesons are reconstructed in
the dimuon final state and are required to have transverse momentum below 1
GeV. The cross-section within the rapidity range of $2.0 < y < 4.5$ is measured
to be $4.45\pm0.24\pm0.18\pm0.58$ mb, where the first uncertainty is
statistical, the second systematic and the third originates from the luminosity
determination. The cross-section is also measured in $J/\psi$ rapidity
intervals. The results are compared to predictions from phenomenological
models.


Abstract:
  Recently, chest X-ray report generation, which aims to automatically generate
descriptions of given chest X-ray images, has received growing research
interests. The key challenge of chest X-ray report generation is to accurately
capture and describe the abnormal regions. In most cases, the normal regions
dominate the entire chest X-ray image, and the corresponding descriptions of
these normal regions dominate the final report. Due to such data bias,
learning-based models may fail to attend to abnormal regions. In this work, to
effectively capture and describe abnormal regions, we propose the Contrastive
Attention (CA) model. Instead of solely focusing on the current input image,
the CA model compares the current input image with normal images to distill the
contrastive information. The acquired contrastive information can better
represent the visual features of abnormal regions. According to the experiments
on the public IU-X-ray and MIMIC-CXR datasets, incorporating our CA into
several existing models can boost their performance across most metrics. In
addition, according to the analysis, the CA model can help existing models
better attend to the abnormal regions and provide more accurate descriptions
which are crucial for an interpretable diagnosis. Specifically, we achieve the
state-of-the-art results on the two public datasets.


Abstract:
  We present a multi-wavelength photometric and spectroscopic analysis of
thirteen "Super-Chandrasekhar Mass"/2003fg-like type Ia Supernova (SNe~Ia).
Nine of these objects were observed by the Carnegie Supernova Project.
2003fg-like have slowly declining light curves ($\Delta m_{15}$(B) $<$1.3 mag),
and peak absolute $B$-band magnitudes between $-19<M_{B}<-21$~mag. Many
2003fg-like are located in the same part of the luminosity width relation as
normal SNe~Ia. In the optical $B$ and $V$ bands, 2003fg-like look like normal
SNe~Ia, but at redder wavelengths they diverge. Unlike other luminous SNe~Ia,
2003fg-like generally have only one $i$-band maximum which peaks after the
epoch of $B$-band maximum, while their NIR light curve rise times can be
$\gtrsim$40 days longer than those of normal SNe~Ia. They are also at least one
magnitude brighter in the NIR bands than normal SNe~Ia, peaking above $M_H <
-19$~mag, and generally have negative Hubble residuals, which may be the cause
of some systematics in dark energy experiments. Spectroscopically, 2003fg-like
exhibit peculiarities such as unburnt carbon well past maximum light, a large
spread (8000--12000~km/s) in SiII $\lambda$6355 velocities at maximum light
with no rapid early velocity decline, and no clear $H$-band break at +10~d, e.
We find that SNe with a larger pseudo equivalent width of CII at maximum light
have lower SiII $\lambda$6355 velocities and slower declining light curves.
There are also multiple factors that contribute to the peak luminosity of
2003fg-like. The explosion of a C-O degenerate core inside a carbon-rich
envelope is consistent with these observations. Such a configuration may come
from the core degenerate scenario.


Abstract:
  In most of neural machine translation distillation or stealing scenarios, the
goal is to preserve the performance of the target model (teacher). The
highest-scoring hypothesis of the teacher model is commonly used to train a new
model (student). If reference translations are also available, then better
hypotheses (with respect to the references) can be upsampled and poor
hypotheses either removed or undersampled.
  This paper explores the importance sampling method landscape (pruning,
hypothesis upsampling and undersampling, deduplication and their combination)
with English to Czech and English to German MT models using standard MT
evaluation metrics. We show that careful upsampling and combination with the
original data leads to better performance when compared to training only on the
original or synthesized data or their direct combination.


Abstract:
  We predict the emergence of novel collective electronic excitations in warm
dense matter with an inhomogeneous electronic structure based on
first-principles calculations. The emerging modes are controlled by the imposed
perturbation amplitude. They include satellite signals around the standard
plasmon feature, transformation of plasmons to optical modes, and
double-plasmon modes. Most importantly, these modes exhibit a pronounced
dependence on the temperature. This makes them potentially invaluable for the
diagnostics of plasma parameters in the warm dense matter regime. We
demonstrate that these modes can be probed with present experimental
techniques.


Abstract:
  We calculate steady-state models of radiation-driven super-Eddington winds
and static expanded envelopes of neutron stars caused by high luminosities in
type I X-ray bursts. We use flux-limited diffusion to model the transition from
optically thick to optically thin, and include effects of general relativity,
allowing us to study the photospheric radius close to the star as the
hydrostatic atmosphere evolves into a wind. We find that the photospheric
radius evolves monotonically from static envelopes ($r_{\rm ph}\lesssim 50-70$
km) to winds ($r_{\rm ph}\approx 100-1000$ km). Photospheric radii of less than
$100$ km, as observed in most photospheric radius expansion bursts, can be
explained by static envelopes, but only in a narrow range of luminosity. In
most bursts, we would expect the luminosity to increase further, leading to a
wind with photospheric radius $\gtrsim 100$ km. In the contraction phase, the
expanded envelope solutions show that the photosphere is still $\approx 1$ km
above the surface when the effective temperature is only $3\%$ away from its
maximum value. This is a possible systematic uncertainty when interpreting the
measured Eddington fluxes from bursts at touchdown. We also discuss the
applicability of steady-state models to describe the dynamics of bursts. In
particular, we show that the sub to super-Eddington transition during the burst
rise is rapid enough that static models are not appropriate. Finally, we
analyze the strength of spectral shifts in our models. Expected shifts at the
photosphere are dominated by gravitational redshift, and are therefore
predicted to be less than a few percent.


Abstract:
  We investigate a generalized form of the phenomenologically emergent dark
energy model, known as generalized emergent dark energy (GEDE), introduced by
Li and Shafieloo [Astrophys. J. {\bf 902}, 58 (2020)] in light of a series of
cosmological probes and considering the evolution of the model at the level of
linear perturbations. This model introduces a free parameter $\Delta$ that can
discriminate between the $\Lambda$CDM (corresponds to $\Delta=0$) or the
phenomenologically emergent dark energy (PEDE) (corresponds to $\Delta=1$)
models, allowing us to determine which model is preferred most by the fit of
the observational datasets. We find evidence in favor of the GEDE model for
Planck alone and in combination with R19, while the Bayesian model comparison
is inconclusive when Supernovae Type Ia or BAO data are included. In
particular, we find that $\Lambda$CDM model is disfavored at more than
$2\sigma$ CL for most of the observational datasets considered in this work and
PEDE is in agreement with Planck 2018+BAO+R19 combination within $1\sigma$ CL.


Abstract:
  In this work we obtain results related to the approximation of
$h$-dimensional dominant subspaces and low rank approximations of matrices $
A\in\mathbb K^{m\times n}$ (where $\mathbb K=\mathbb R$ or $\mathbb C)$ in case
there is no singular gap at the index $h$, i.e. if $\sigma_h=\sigma_{h+1}$
(where $\sigma_1\geq \ldots\geq \sigma_p\geq 0$ denote the singular values of $
A$, and $p=\min\{m,n\}$). In order to do this, we develop a novel perspective
for the convergence analysis of the classical deterministic block Krylov
methods in this context. Indeed, starting with a matrix $ X\in\mathbb
K^{n\times r}$ with $r\geq h$ satisfying a compatibility assumption with some
$h$-dimensional right dominant subspace, we show that block Krylov methods
produce arbitrarily good approximations for both problems mentioned above. Our
approach is based on recent work by Drineas, Ipsen, Kontopoulou and
Magdon-Ismail on approximation of structural left dominant subspaces. The main
difference between our work and previous work on this topic is that instead of
exploiting a singular gap at $h$ (which is zero in this case) we exploit the
nearest existing singular gaps.


Abstract:
  We investigate adiabatic solutions to general relativity for a spacetime with
spatial slices with boundary, by Manton approximation. This approximation tells
us for a theory with a Lagrangian in the natural form, a motion that is
described as a slow motion on the space of vacua-static solutions that minimize
the energy -- is a good approximate solution. To apply this to the case of
general relativity we first bring it to the natural form by splitting space and
time and choosing Gaussian normal coordinates, where a spacetime is described
by the metric on its spatial slices. Then following Manton we propose slow
solutions such that each slice is a slowly changing diffeomorphism of a
reference slice, and thus each solution is described by a vector field on the
spatial slice.
  These solutions will have the property that the action will become a
functional of the vector fields on the boundaries of the spatial slices.
Moreover using the Hodge-Morrey-Friedrichs decomposition we will show that the
constraints of general relativity will identify a unique solution for a given
boundary value. Then we comment on the structure of the space of vacua which we
show to be a (pseudo)-Riemannian homogeneous space. We illustrate our procedure
for a specific reference slice we choose: the 3d Euclidean round ball.


Abstract:
  The 19-dimensional TP06 cardiac muscle cell model is reduced to a
17-dimensional version, which satisfies the required conditions for performing
an analysis of its dynamics by means of bifurcation theory. The reformulated
model is shown to be a good approximation of the original one. As a
consequence, one can extract fairly precise predictions of the behaviour of the
original model from the bifurcation analysis of the modified model. Thus, the
findings of bifurcations linked to complex dynamics in the modified model -
like early afterdepolarisations (EADs), which can be precursors to cardiac
death - predicts the occurrence of the same dynamics in the original model. It
is shown that bifurcations linked to EADs in the modified model accurately
predicts EADs in the original model at the single cell level. Finally, these
bifurcations are linked to cardiac death at the tissue level by example.


Abstract:
  The dominant site of production of $r$-process elements remains unclear
despite recent observations of a neutron star merger. Observational constraints
on the properties of the sites can be obtained by comparing $r$-process
abundances in different environments. The recent Gaia data releases and large
samples from high-resolution optical spectroscopic surveys are enabling us to
compare $r$-process element abundances between stars formed in an accreted
dwarf galaxy, Gaia-Enceladus, and those formed in the Milky Way. We aim to
understand the origin of $r$-process elements in Gaia-Enceladus. We first
construct a sample of stars to study Eu abundances without being affected by
the detection limit. We then kinematically select 71 Gaia-Enceladus stars and
93 in-situ stars from the Galactic Archaeology with HERMES (GALAH) DR3, of
which 50 and 75 stars can be used to study Eu reliably. Gaia-Enceladus stars
clearly show higher ratios of [{Eu}/{Mg}] than in-situ stars. High [{Eu}/{Mg}]
along with low [{Mg}/{Fe}] are also seen in relatively massive satellite
galaxies such as the LMC, Fornax, and Sagittarius dwarfs. On the other hand,
unlike these galaxies, Gaia-Enceladus does not show enhanced [{Ba}/{Eu}] or
[{La}/{Eu}] ratios suggesting a lack of significant $s$-process contribution.
From comparisons with simple chemical evolution models, we show that the high
[{Eu}/{Mg}] of Gaia-Enceladus can naturally be explained by considering
$r$-process enrichment by neutron-star mergers with delay time distribution
that follows a similar power-law as type~Ia supernovae but with a shorter
minimum delay time.


Abstract:
  We propose an intrinsic 3D Fabry-P\'erot type interferometer, coined
"higher-order interferometer", that utilizes the chiral hinge states of
second-order topological insulators and cannot be equivalently mapped to 2D
space because of higher-order topology. Quantum interference patterns in the
two-terminal conductance of this interferometer are controllable not only by
tuning the strength but also, particularly, by rotating the direction of the
magnetic field applied perpendicularly to the transport direction. Remarkably,
the conductance exhibits a characteristic beating pattern with multiple
frequencies with respect to field strength or direction. Our novel
interferometer provides feasible and robust magneto-transport signatures to
probe the particular hinge states of higher-order topological insulators.


Abstract:
  In this article, we propose the approach to procedural optimization of a
neural network, based on the combination of information theory and braid
theory. The network studied in the article implemented with the intersections
between the braid strands, as well as simplified networks (a network with
strands without intersections and a simple convolutional deep neural network),
are used to solve various problems of multiclass image classification that
allow us to analyze the comparative effectiveness of the proposed architecture.
The simulation results showed BraidNet's comparative advantage in learning
speed and classification accuracy.


Abstract:
  This work oversees with the coupled Schr\"odinger-Boussinesq equations with
conformable derivative, which have lots of applications in laser and plasma.
The said equations are reduced to a coupled stationary form using complex
travelling wave transformation. Next Painlev\'e test applied to derived the
integrable cases of the reduced equation, after that using RCAM derived the
solution of reduced equations integrable and nonintegrable cases. Few theorems
have been presented and proved to ensure their boundedness. All presented
boundedness cases have been checked and explained by plotting the solutions for
particulars values of parameters satisfying them. The obtained solutions of
stationary form utilized to derive solutions of the coupled
Schr\"odinger-Boussinesq equations with conformable derivative. The derived
solutions have been plotted and explained. From this, it appears that these
solutions propagate by maintaining their two-hump, W-shaped, M-shaped solutions
shapes.


Abstract:
  Logistics has gained great attentions with the prosperous development of
commerce, which is often seen as the classic optimal vehicle routing problem.
Meanwhile, electric vehicle (EV) has been widely used in logistic fleet to curb
the emission of green house gases in recent years. Solving the optimization
problem of joint routing and charging of multiple EVs is in a urgent need,
whose objective function includes charging time, charging cost, EVs travel
time, usage fees of EV and revenue from serving customers. This joint problem
is formulated as a mixed integer programming (MIP) problem, which, however, is
NP-hard due to integer restrictions and bilinear terms from the coupling
between routing and charging decisions. The main contribution of this paper
lies at proposing an efficient two stage algorithm that can decompose the
original MIP problem into two linear programming (LP) problems, by exploiting
the exactness of LP relaxation and eliminating the coupled term. This algorithm
can achieve a nearoptimal solution in polynomial time. In addition, another
variant algorithm is proposed based on the two stage one, to further improve
the quality of solution.


Abstract:
  In this paper we present a rigorous derivation of the Boltzmann equation in a
compact domain with diffuse reflection boundary conditions. We consider a
system of $N$ hard spheres of diameter $\epsilon$ in a box $\Lambda := [0, 1]
\times (R/Z)^2$. When a particle meets the boundary of the domain, it is
instantaneously reinjected into the box with a random direction, and conserving
kinetic energy. We prove that the first marginal of the process converges in
the scaling $N\epsilon^2 = 1$, $\epsilon\rightarrow 0$ to the solution of the
Boltzmann equation, with the same short time restriction of Lanford's classical
theorem.


Abstract:
  It is widely believed that consistent theories of quantum gravity satisfy two
basic kinematic constraints: they are free from any global symmetry, and they
contain a complete spectrum of gauge charges. For compact, abelian gauge
groups, completeness follows from the absence of a 1-form global symmetry.
However, this correspondence breaks down for more general gauge groups, where
the breaking of the 1-form symmetry is insufficient to guarantee a complete
spectrum. We show that the correspondence may be restored by broadening our
notion of symmetry to include non-invertible topological operators, and prove
that their absence is sufficient to guarantee a complete spectrum for any
compact, possibly disconnected gauge group. In addition, we prove an analogous
statement regarding the completeness of twist vortices: codimension-2 objects
defined by a discrete holonomy around their worldvolume, such as cosmic strings
in four dimensions. We discuss how this correspondence is modified in various,
more general contexts, including non-compact gauge groups, Higgsing of gauge
theories, and the addition of Chern-Simons terms. Finally, we discuss the
implications of our results for the Swampland program, as well as the
phenomenological implications of the existence of twist strings.


Abstract:
  Neutrinos are particles that interact rarely, so identifying them requires
large detectors which produce lots of data. Processing this data with the
computing power available is becoming even more difficult as the detectors
increase in size to reach their physics goals. Liquid argon time projection
chamber (LArTPC) neutrino experiments are expected to grow in the next decade
to have 100 times more wires than in currently operating experiments, and
modernization of LArTPC reconstruction code, including parallelization both at
data- and instruction-level, will help to mitigate this challenge. The LArTPC
hit finding algorithm is used across multiple experiments through a common
software framework. In this paper we discuss a parallel implementation of this
algorithm. Using a standalone setup we find speed up factors of two times from
vectorization and 30--100 times from multi-threading on Intel architectures.
The new version has been incorporated back into the framework so that it can be
used by experiments. On a serial execution, the integrated version is about 10
times faster than the previous one and, once parallelization is enabled,
further speedups comparable to the standalone program are achieved.


Abstract:
  In contrast with the 3-dimensional case (cf. \cite{RaMo}), where rotationally
symmetric totally geodesic free boundary minimal surfaces have Morse index one;
we prove in this work that the Morse index of a free boundary rotationally
symmetric totally geodesic hypersurface of the $n$-dimensional Riemannnian
Schwarzschild space with respect to variations that are tangential along the
horizon is zero, for $n\geq4$.
  Moreover, we show that there exist non-compact free boundary minimal
hypersurfaces which are not totally geodesic, $n\geq 8$, with Morse index equal
to $0$. Also, it is shown that, for $n\geq4$, there exist infinitely many
non-compact free boundary minimal hypersurfaces, which are not congruent to
each other, with infinite Morse index.
  We also study the density at infinity of a free boundary minimal hypersurface
with respect to a minimal cone constructed over a minimal hypersurface of the
unit Euclidean sphere. We obtain a lower bound for the density in terms of the
area of the boundary of the hypersurface and the area of the minimal
hypersurface in the unit sphere. This lower bound is optimal in the sense that
only minimal cones achieve it.


Abstract:
  Android is one of the leading operating systems for smart phones in terms of
market share and usage. Unfortunately, it is also an appealing target for
attackers to compromise its security through malicious applications. To tackle
this issue, domain experts and researchers are trying different techniques to
stop such attacks. All the attempts of securing Android platform are somewhat
successful. However, existing detection techniques have severe shortcomings,
including the cumbersome process of feature engineering. Designing
representative features require expert domain knowledge. There is a need for
minimizing human experts' intervention by circumventing handcrafted feature
engineering. Deep learning could be exploited by extracting deep features
automatically. Previous work has shown that operational codes (opcodes) of
executables provide key information to be used with deep learning models for
detection process of malicious applications. The only challenge is to feed
opcodes information to deep learning models. Existing techniques use one-hot
encoding to tackle the challenge. However, the one-hot encoding scheme has
severe limitations. In this paper, we introduce; (1) a novel technique for
opcodes embedding, which we name Op2Vec, (2) based on the learned Op2Vec we
have developed a dataset for end-to-end detection of android malware.
Introducing the end-to-end Android malware detection technique avoids
expert-intensive handcrafted features extraction, and ensures automation. The
comparison shows that Op2Vec outperforms the existing one-hot encoding
technique for opcode embedding and the developed dataset can provide
significant insights for end-to-end detection of Android malware.


Abstract:
  We define Kakeya sets in the Heisenberg group and show that the Heisenberg
Hausdorff dimension of Kakeya sets in the first Heisenberg group is at least 3.
This lower bound is sharp since, under our definition, the $\{xoy\}$-plane is a
Kakeya set with Heisenberg Hausdorff dimension 3.


Abstract:
  In recent years there has been significant fundamental research into phonon
polaritons, owing to their ability to compress light to extremely small
dimensions, low-losses, and ability to support anisotropic propagation. In this
perspective, after briefly reviewing the present state of mid-infrared
optoelectronics, we will assess the potential of phonon-polariton based
nanophotonics for infrared (3-100$\mathrm{\mu}$m) light sources, detectors and
modulators. These will operate in the Reststrahlen region where conventional
semiconductor light sources become ineffective. Drawing on the results from the
past few years, we will sketch some promising paths to create such devices and
we will evaluate their practical advantages and disadvantages when compared to
other approaches to infrared optoelectronics.


Abstract:
  Most fair regression algorithms mitigate bias towards sensitive sub
populations and therefore improve fairness at group level. In this paper, we
investigate the impact of such implementation of fair regression on the
individual. More precisely, we assess the evolution of continuous predictions
from an unconstrained to a fair algorithm by comparing results from baseline
algorithms with fair regression algorithms for the same data points. Based on
our findings, we propose a set of post-processing algorithms to improve the
utility of the existing fair regression approaches.


Abstract:
  Noise and low quality of ECG signals acquired from Holter or wearable devices
deteriorate the accuracy and robustness of R-peak detection algorithms. This
paper presents a generic and robust system for R-peak detection in Holter ECG
signals. While many proposed algorithms have successfully addressed the problem
of ECG R-peak detection, there is still a notable gap in the performance of
these detectors on such low-quality ECG records. Therefore, in this study, a
novel implementation of the 1D Convolutional Neural Network (CNN) is used
integrated with a verification model to reduce the number of false alarms. This
CNN architecture consists of an encoder block and a corresponding decoder block
followed by a sample-wise classification layer to construct the 1D segmentation
map of R- peaks from the input ECG signal. Once the proposed model has been
trained, it can solely be used to detect R-peaks possibly in a single channel
ECG data stream quickly and accurately, or alternatively, such a solution can
be conveniently employed for real-time monitoring on a lightweight portable
device. The model is tested on two open-access ECG databases: The China
Physiological Signal Challenge (2020) database (CPSC-DB) with more than one
million beats, and the commonly used MIT-BIH Arrhythmia Database (MIT-DB).
Experimental results demonstrate that the proposed systematic approach achieves
99.30% F1-score, 99.69% recall, and 98.91% precision in CPSC-DB, which is the
best R-peak detection performance ever achieved. Compared to all competing
methods, the proposed approach can reduce the false-positives and
false-negatives in Holter ECG signals by more than 54% and 82%, respectively.
Results also demonstrate similar or better performance than most competing
algorithms on MIT-DB with 99.83% F1-score, 99.85% recall, and 99.82% precision.