Abstract:
  The adaptive changes in synaptic efficacy that occur between spiking neurons
have been demonstrated to play a critical role in learning for biological
neural networks. Despite this source of inspiration, many learning focused
applications using Spiking Neural Networks (SNNs) retain static synaptic
connections, preventing additional learning after the initial training period.
Here, we introduce a framework for simultaneously learning the underlying
fixed-weights and the rules governing the dynamics of synaptic plasticity and
neuromodulated synaptic plasticity in SNNs through gradient descent. We further
demonstrate the capabilities of this framework on a series of challenging
benchmarks, learning the parameters of several plasticity rules including BCM,
Oja's, and their respective set of neuromodulatory variants. The experimental
results display that SNNs augmented with differentiable plasticity are
sufficient for solving a set of challenging temporal learning tasks that a
traditional SNN fails to solve, even in the presence of significant noise.
These networks are also shown to be capable of producing locomotion on a
high-dimensional robotic learning task, where near-minimal degradation in
performance is observed in the presence of novel conditions not seen during the
initial training period.


Abstract:
  The emergent induction of spiral magnets that was proposed [Jpn. J. Appl.
Phys. 58, 120909 (2019)] and recently demonstrated [Nature 586, 232 (2020)] is
shown to be further extended by a comprehensive treatment of the Rashba
spin-orbit coupling and the electron spin relaxation that affect the underlying
processes of spin-transfer torque and spinmotive force. Within adiabatic
approximation, we show that the output voltages are widely altered
intrinsically via the Rashba effect whereas extrinsically via the nonadiabatic
correction due to the spin relaxation and sample disorder. The findings
respectively clarify the origins for the amplitude modulation and sign change
of the emergent inductance with tunability by electrical gating and careful
sample preparation.


Abstract:
  Nonlinear dynamics of an optical pulse or a beam continue to be one of the
active areas of research in the field of optical solitons. Especially, in
multi-mode fibers or fiber arrays and photorefractive materials, the vector
solitons display rich nonlinear phenomena. Due to their fascinating and
intriguing novel properties, the theory of optical vector solitons has been
developed considerably both from theoretical and experimental points of view
leading to soliton based promising potential applications. In the recent past,
many types of vector solitons have been identified both in the integrable and
non-integrable coupled nonlinear Schr\"{o}dinger (CNLS) equations framework. In
this article, we review some of the recent progress in understanding the
dynamics of the so called nondegenerate vector bright solitons in nonlinear
optics, where the fundamental soliton can have more than one propagation
constant. We address this theme by considering the integrable two CNLS family
of equations, namely Manakov system, mixed 2-CNLS system, coherently CNLS
system, generalized CNLS system and two-component long-wave short-wave
resonance interaction (LSRI) system. In these models, we discuss the existence
of nondegenerate vector solitons and their associated novel multi-hump
geometrical profile nature by deriving their analytical forms through the
Hirota bilinear method. Then we reveal the novel collision properties of the
nondegenerate solitons in the Manakov system as an example. The asymptotic
analysis shows that the nondegenerate solitons, in general, undergo three types
of elastic collisions without any energy redistribution among the modes.
Further, we show that the energy sharing collision exhibiting vector solitons
arises as a special case of the newly reported nondegenerate vector solitons.
Finally, we point out the possible further developments in this subject and
potential applications.


Abstract:
  Flow in fractured porous media is modeled frequently by discrete
fracture-matrix approaches where fractures are treated as dimensionally reduced
manifolds. Generalizing earlier work we focus on two-phase flow in
time-dependent fracture geometries including the fracture's aperture. We
present the derivation of a reduced model for immiscible two-phase flow in
porous media. For the reduced model we present a fully conforming finite-volume
discretization coupled with a moving-mesh method. This method permits arbitrary
movement of facets of the triangulation while being fully conservative. In
numerical examples we show the performance of the scheme and investigate the
modeling error of the reduced model.


Abstract:
  Estimation of bone age from hand radiographs is essential to determine
skeletal age in diagnosing endocrine disorders and depicting the growth status
of children. However, existing automatic methods only apply their models to
test images without considering the discrepancy between training samples and
test samples, which will lead to a lower generalization ability. In this paper,
we propose an adversarial regression learning network (ARLNet) for bone age
estimation. Specifically, we first extract bone features from a fine-tuned
Inception V3 neural network and propose regression percentage loss for
training. To reduce the discrepancy between training and test data, we then
propose adversarial regression loss and feature reconstruction loss to
guarantee the transition from training data to test data and vice versa,
preserving invariant features from both training and test data. Experimental
results show that the proposed model outperforms state-of-the-art methods.


Abstract:
  Large-scale unlabeled data has allowed recent progress in self-supervised
learning methods that learn rich visual representations. State-of-the-art
self-supervised methods for learning representations from images (MoCo and
BYOL) use an inductive bias that different augmentations (e.g. random crops) of
an image should produce similar embeddings. We show that such methods are
vulnerable to backdoor attacks where an attacker poisons a part of the
unlabeled data by adding a small trigger (known to the attacker) to the images.
The model performance is good on clean test images but the attacker can
manipulate the decision of the model by showing the trigger at test time.
Backdoor attacks have been studied extensively in supervised learning and to
the best of our knowledge, we are the first to study them for self-supervised
learning. Backdoor attacks are more practical in self-supervised learning since
the unlabeled data is large and as a result, an inspection of the data to avoid
the presence of poisoned data is prohibitive. We show that in our targeted
attack, the attacker can produce many false positives for the target category
by using the trigger at test time. We also propose a knowledge distillation
based defense algorithm that succeeds in neutralizing the attack. Our code is
available here: https://github.com/UMBCvision/SSL-Backdoor .


Abstract:
  Design Fiction is known for its provocative and often dystopian speculations
about the future. In this paper, we present an alternative approach that
focuses primarily on the positive. We propose to imagine, enact, and evaluate
utopia with participants. By doing so, we react to four main critiques
concerning Design Fiction: (1) its negativity, (2) its contextlessness, (3) its
elitist authorship, and (4) its missing evaluation methods.


Abstract:
  In e-commerce advertising, it is crucial to jointly consider various
performance metrics, e.g., user experience, advertiser utility, and platform
revenue. Traditional auction mechanisms, such as GSP and VCG auctions, can be
suboptimal due to their fixed allocation rules to optimize a single performance
metric (e.g., revenue or social welfare). Recently, data-driven auctions,
learned directly from auction outcomes to optimize multiple performance
metrics, have attracted increasing research interests. However, the procedure
of auction mechanisms involves various discrete calculation operations, making
it challenging to be compatible with continuous optimization pipelines in
machine learning. In this paper, we design \underline{D}eep \underline{N}eural
\underline{A}uctions (DNAs) to enable end-to-end auction learning by proposing
a differentiable model to relax the discrete sorting operation, a key component
in auctions. We optimize the performance metrics by developing deep models to
efficiently extract contexts from auctions, providing rich features for auction
design. We further integrate the game theoretical conditions within the model
design, to guarantee the stability of the auctions. DNAs have been successfully
deployed in the e-commerce advertising system at Taobao. Experimental
evaluation results on both large-scale data set as well as online A/B test
demonstrated that DNAs significantly outperformed other mechanisms widely
adopted in industry.


Abstract:
  We show how to compute electromagnetic polarizabilities of charged hadrons
using four-point functions in lattice QCD. The low-energy behavior of Compton
scattering amplitude is matched to matrix elements of current-current
correlation functions on the lattice. Working in momentum space, formulas for
electric polarizability ($\alpha_E$) and magnetic polarizability ($\beta_M$)
are derived for both charged pion and proton. Lattice four-point correlation
functions are constructed from quark and gluon fields to be used in Monte-Carlo
simulations. The content of the functions is assessed in detail and specific
prescriptions are given to isolate the polarizabilities. The connected
quark-line diagrams can be done today as a small lattice project. The
disconnected diagrams are more challenging but are within reach of dedicated
resources for medium to large lattice projects. We also draw attention to the
potential of four-point functions as a multi-purpose tool for hadron structure.


Abstract:
  A celebrated unresolved conjecture of Erd\"{o}s and Hajnal states that for
every undirected graph $H$ there exists $ \epsilon(H) > 0 $ such that every
undirected graph on $ n $ vertices that does not contain $H$ as an induced
subgraph contains a clique or a stable set of size at least $ n^{\epsilon(H)}
$. This conjecture has a directed equivalent version stating that for every
tournament $H$ there exists $ \epsilon(H) > 0 $ such that every $H-$free
$n-$vertex tournament $T$ contains a transitive subtournament of size at least
$ n^{\epsilon(H)} $. Recently the conjecture was proved for all six-vertex
tournaments, except $K_{6}$. In this paper we construct two infinite families
of tournaments for which the conjecture is still open for infinitely many
tournaments in these two families $-$ the family of so-called super nebulas and
the family of so-called super triangular galaxies. We prove that for every
super nebula $H_{1}$ and every $\Delta$galaxy $H_{2}$ there exist
$\epsilon(H_{1},H_{2})$ such that every $\lbrace H_{1},H_{2}\rbrace$$-$free
tournament $T$ contains a transitive subtournament of size at least
$\mid$$T$$\mid^{\epsilon(H_{1},H_{2})}$. We also prove that for every central
triangular galaxy $H$ there exist $\epsilon(K_{6},H)$ such that every $\lbrace
K_{6},H\rbrace$$-$free tournament $T$ contains a transitive subtournament of
size at least $\mid$$T$$\mid^{\epsilon(K_{6},H)}$. And we give an extension of
our results.


Abstract:
  We study the sub-grid scale characteristics of a vorticity-transport-based
approach for large-eddy simulations. In particular, we consider a
multi-dimensional upwind scheme for the vorticity transport equations and
establish its properties in the under-resolved regime. The asymptotic behavior
of key turbulence statistics of velocity gradients, vorticity, and invariants
is studied in detail. Modified equation analysis indicates that dissipation can
be controlled locally via non-linear limiting of the gradient employed for the
vorticity reconstruction on the cell face such that low numerical diffusion is
obtained in well-resolved regimes and high numerical diffusion is realized in
under-resolved regions. The enstrophy budget highlights the remarkable ability
of the truncation terms to mimic the true sub-grid scale dissipation and
diffusion. The modified equation also reveals diffusive terms that are similar
to several commonly employed sub-grid scale models including tensor-gradient
and hyper-viscosity models. Investigations on several canonical turbulence flow
cases show that large-scale features are adequately represented and remain
consistent in terms of spectral energy over a range of grid resolutions.
Numerical dissipation in under-resolved simulations is consistent and can be
characterized by diffusion terms discovered in the modified equation analysis.
A minimum state of scale separation necessary to obtain asymptotic behavior is
characterized using metrics such as effective Reynolds number and effective
grid spacing. Temporally-evolving jet simulations, characterized by large-scale
vortical structures, demonstrate that high Reynolds number vortex-dominated
flows are captured when criteria is met and necessitate diffusive non-linear
limiting of vorticity reconstruction be employed to realize accuracy in
under-resolved simulations.


Abstract:
  The concept of thermal machines has evolved from the canonical steam engine
to the recently proposed nanoscopic quantum systems as working fluids. The
latter obey quantum open system dynamics and frequently operate in
non-equilibrium conditions. However, the role of this dynamics in the overall
performance of quantum heat engines remains an open problem. Here, we analyse
and optimize the efficiency and power output of two-stage quantum heat engines
fuelled by non-equilibrium steady states. In a charging first stage, the
quantum working fluid consisting of a qutrit or two coupled qubits is connected
to two reservoirs at different temperatures, which establish a heat current
that stores ergotropy in the system; the second stage comprises a coherent
driving force that extracts work from the machine in finite a amount of time;
finally, the external drive is switched off and the machine enters a new cycle.


Abstract:
  The interaction of spin-polarized one-dimensional (1D) topological edge modes
localized along single-atomic steps of the topological crystalline insulator
$Pb_{0.7}Sn_{0.3}Se(001)$ has been studied systematically by scanning tunneling
spectroscopy. Our results reveal that the coupling of adjacent edge modes sets
in at a step{to{step distance $d_{ss} < 25$ nm, resulting in a characteristic
splitting of a single peak at the Dirac point in tunneling spectra. Whereas the
energy splitting exponentially increases with decreasing $d_{ss}$ for
single-atomic steps running almost parallel, we find no splitting for
single-atomic step edges under an angle of $90^{o}$. The results are discussed
in terms of overlapping wave functions with $p_x; p_y$ orbital character.


Abstract:
  We consider the regression problem of estimating functions on $\mathbb{R}^D$
but supported on a $d$-dimensional manifold $ \mathcal{M} \subset \mathbb{R}^D
$ with $ d \ll D $. Drawing ideas from multi-resolution analysis and nonlinear
approximation, we construct low-dimensional coordinates on $\mathcal{M}$ at
multiple scales, and perform multiscale regression by local polynomial fitting.
We propose a data-driven wavelet thresholding scheme that automatically adapts
to the unknown regularity of the function, allowing for efficient estimation of
functions exhibiting nonuniform regularity at different locations and scales.
We analyze the generalization error of our method by proving finite sample
bounds in high probability on rich classes of priors. Our estimator attains
optimal learning rates (up to logarithmic factors) as if the function was
defined on a known Euclidean domain of dimension $d$, instead of an unknown
manifold embedded in $\mathbb{R}^D$. The implemented algorithm has quasilinear
complexity in the sample size, with constants linear in $D$ and exponential in
$d$. Our work therefore establishes a new framework for regression on
low-dimensional sets embedded in high dimensions, with fast implementation and
strong theoretical guarantees.


Abstract:
  We show how to characterize the indistinguishability of up to four identical,
bosonic or fermionic particles, which are rendered partially distinguishable
through their internal degrees of freedom prepared in mixed states. This is
accomplished via their counting statistics when subjected to a highly symmetric
unitary acting upon their external (i.e., dynamical) degrees of freedom. For
pure internal states, we further extract information on the particles'
collective phases, which ultimately allows for an experimental reconstruction
of the full many-particle density operator up to complex conjugation.


Abstract:
  Twisted van der Waals materials have been shown to host a variety of tunable
electronic structures. Here we put forward twisted trilayer graphene (TTG) as a
platform to emulate heavy fermion physics. We demonstrate that TTG hosts
extended and localized modes with an electronic structure that can be
controlled by interlayer bias. In the presence of interactions, the existence
of localized modes leads to the development of local moments, which are Kondo
coupled to coexisting extended states. By electrically controlling the
effective exchange between local moments, the system can be driven from a
magnetic into a heavy fermion regime, passing through a quantum critical point.
Our results put forward twisted graphene multilayers as a platform for the
realization of strongly correlated heavy fermion physics in a purely
carbon-based platform.


Abstract:
  The paper studies planar surface reconstruction of indoor scenes from two
views with unknown camera poses. While prior approaches have successfully
created object-centric reconstructions of many scenes, they fail to exploit
other structures, such as planes, which are typically the dominant components
of indoor scenes. In this paper, we reconstruct planar surfaces from multiple
views, while jointly estimating camera pose. Our experiments demonstrate that
our method is able to advance the state of the art of reconstruction from
sparse views, on challenging scenes from Matterport3D. Project site:
https://jinlinyi.github.io/SparsePlanes/


Abstract:
  Traditional approaches to ensure group fairness in algorithmic decision
making aim to equalize ``total'' error rates for different subgroups in the
population. In contrast, we argue that the fairness approaches should instead
focus only on equalizing errors arising due to model uncertainty (a.k.a
epistemic uncertainty), caused due to lack of knowledge about the best model or
due to lack of data. In other words, our proposal calls for ignoring the errors
that occur due to uncertainty inherent in the data, i.e., aleatoric
uncertainty. We draw a connection between predictive multiplicity and model
uncertainty and argue that the techniques from predictive multiplicity could be
used to identify errors made due to model uncertainty. We propose scalable
convex proxies to come up with classifiers that exhibit predictive multiplicity
and empirically show that our methods are comparable in performance and up to
four orders of magnitude faster than the current state-of-the-art. We further
propose methods to achieve our goal of equalizing group error rates arising due
to model uncertainty in algorithmic decision making and demonstrate the
effectiveness of these methods using synthetic and real-world datasets.


Abstract:
  We discuss unpolarized neutrino- and anti-neutrino-nucleon deep inelastic
scattering (DIS) using a chiral doublet of baryonic sources with explicit
symmetry breaking, in a slice of AdS$_5$ with both a hard and soft wall. We
explicitly derive the direct and transition form factors for the vector and
axial-vector currents for the holographic dual of a proton and neutron. We use
them to derive the s-channel structure functions for neutrino and anti-neutrino
scattering on a proton and neutron in bulk. The t-channel contributions
stemming from the Pomeron and Reggeon exchanges are also evaluated explicitly.
The pertinent even and odd structure functions in the limit of large and small
parton momentum fraction $x$ are given. The results allow for the extraction of
the nonperterbative parton distribution functions carried by the sea and
valence quarks both at large-x and small-x regimes. Our holographic PDF sets
compare well with LHAPDF and CTEQ PDF sets in the large-x and small-x regimes
in the intermediate range of $Q^2<10~\rm{GeV^2}$.


Abstract:
  Emotional expressions are the behaviors that communicate our emotional state
or attitude to others. They are expressed through verbal and non-verbal
communication. Complex human behavior can be understood by studying physical
features from multiple modalities; mainly facial, vocal and physical gestures.
Recently, spontaneous multi-modal emotion recognition has been extensively
studied for human behavior analysis. In this paper, we propose a new deep
learning-based approach for audio-visual emotion recognition. Our approach
leverages recent advances in deep learning like knowledge distillation and
high-performing deep architectures. The deep feature representations of the
audio and visual modalities are fused based on a model-level fusion strategy. A
recurrent neural network is then used to capture the temporal dynamics. Our
proposed approach substantially outperforms state-of-the-art approaches in
predicting valence on the RECOLA dataset. Moreover, our proposed visual facial
expression feature extraction network outperforms state-of-the-art results on
the AffectNet and Google Facial Expression Comparison datasets.


Abstract:
  Economic Policy Uncertainty (EPU) is a critical indicator in economic
studies, while it can be used to forecast a recession. Under higher levels of
uncertainty, firms' owners cut their investment, which leads to a longer
post-recession recovery. EPU index is computed by counting news articles
containing pre-defined keywords related to policy-making and economy and convey
uncertainty. Unfortunately, this method is sensitive to the original keyword
set, its richness, and the news coverage. Thus, reproducing its results for
different countries is challenging. In this paper, we propose an unsupervised
text mining method that uses word-embedding representation space to select
relevant keywords. This method is not strictly sensitive to the semantic
similarity threshold applied to the word embedding vectors and does not require
a pre-defined dictionary. Our experiments using a massive repository of Persian
news show that the EPU series computed by the proposed method precisely follows
major events affecting Iran's economy and is compatible with the World
Uncertainty Index (WUI) of Iran.


Abstract:
  During the past few years, mostly as a result of the GDPR and the CCPA,
websites have started to present users with cookie consent banners. These
banners are web forms where the users can state their preference and declare
which cookies they would like to accept, if such option exists. Although
requesting consent before storing any identifiable information is a good start
towards respecting the user privacy, yet previous research has shown that
websites do not always respect user choices. Furthermore, considering the ever
decreasing reliance of trackers on cookies and actions browser vendors take by
blocking or restricting third-party cookies, we anticipate a world where
stateless tracking emerges, either because trackers or websites do not use
cookies, or because users simply refuse to accept any.
  In this paper, we explore whether websites use more persistent and
sophisticated forms of tracking in order to track users who said they do not
want cookies. Such forms of tracking include first-party ID leaking, ID
synchronization, and browser fingerprinting. Our results suggest that websites
do use such modern forms of tracking even before users had the opportunity to
register their choice with respect to cookies. To add insult to injury, when
users choose to raise their voice and reject all cookies, user tracking only
intensifies. As a result, users' choices play very little role with respect to
tracking: we measured that more than 75% of tracking activities happened before
users had the opportunity to make a selection in the cookie consent banner, or
when users chose to reject all cookies.


Abstract:
  Free-space optics (FSO) offer promising solutions in wireless systems where
many different devices need to communicate and interoperate, exchanging massive
amounts of data as in communication and sensor networks, and in technologies
for positioning and ranging. In these applications, control and manipulation of
the light field is required for properly generating and correctly receiving FSO
beams even in the presence of unpredictable objects and turbulence in the light
path. In this work, we use a programmable mesh of Mach-Zehnder Interferometers
(MZIs) to automatically control the complex field radiated and captured by an
array of optical antennas. Several functionalities are demonstrated, including
the generation of perfectly shaped beams with non-perfect optical antennas, the
imaging of a desired field pattern through an obstacle or a diffusive medium,
and the identification of an unknown obstacle inserted in the FSO path.
Compared to conventional devices used for the manipulation of FSO beams, such
as spatial light modulators (SLMs), our programmable circuit can self-configure
through automated control strategies and can be integrated with other
functionalities implemented onto the same photonic chip.


Abstract:
  Recently, there has been discussions on the ill-posed nature of
super-resolution that multiple possible reconstructions exist for a given
low-resolution image. Using normalizing flows, SRflow[23] achieves
state-of-the-art perceptual quality by learning the distribution of the output
instead of a deterministic output to one estimate. In this paper, we adapt the
concepts of SRFlow to improve GAN-based super-resolution by properly
implementing the one-to-many property. We modify the generator to estimate a
distribution as a mapping from random noise. We improve the content loss that
hampers the perceptual training objectives. We also propose additional training
techniques to further enhance the perceptual quality of generated images. Using
our proposed methods, we were able to improve the performance of ESRGAN[1] in
x4 perceptual SR and achieve the state-of-the-art LPIPS score in x16 perceptual
extreme SR by applying our methods to RFB-ESRGAN[21].


Abstract:
  Deep learning has shown its power in many applications, including object
detection in images, natural-language understanding, and speech recognition. To
make it more accessible to end users, many deep learning models are now
embedded in mobile apps. Compared to offloading deep learning from smartphones
to the cloud, performing machine learning on-device can help improve latency,
connectivity, and power consumption. However, most deep learning models within
Android apps can easily be obtained via mature reverse engineering, while the
models' exposure may invite adversarial attacks. In this study, we propose a
simple but effective approach to hacking deep learning models using adversarial
attacks by identifying highly similar pre-trained models from TensorFlow Hub.
All 10 real-world Android apps in the experiment are successfully attacked by
our approach. Apart from the feasibility of the model attack, we also carry out
an empirical study that investigates the characteristics of deep learning
models used by hundreds of Android apps on Google Play. The results show that
many of them are similar to each other and widely use fine-tuning techniques to
pre-trained models on the Internet.


Abstract:
  Sound event detection (SED) is the task of tagging the absence or presence of
audio events and their corresponding interval within a given audio clip. While
SED can be done using supervised machine learning, where training data is fully
labeled with access to per event timestamps and duration, our work focuses on
weakly-supervised sound event detection (WSSED), where prior knowledge about an
event's duration is unavailable. Recent research within the field focuses on
improving segment- and event-level localization performance for specific
datasets regarding specific evaluation metrics. Specifically, well-performing
event-level localization requires fully labeled development subsets to obtain
event duration estimates, which significantly benefits localization
performance. Moreover, well-performing segment-level localization models output
predictions at a coarse-scale (e.g., 1 second), hindering their deployment on
datasets containing very short events (< 1 second). This work proposes a
duration robust CRNN (CDur) framework, which aims to achieve competitive
performance in terms of segment- and event-level localization. This paper
proposes a new post-processing strategy named "Triple Threshold" and
investigates two data augmentation methods along with a label smoothing method
within the scope of WSSED. Evaluation of our model is done on the DCASE2017 and
2018 Task 4 datasets, and URBAN-SED. Our model outperforms other approaches on
the DCASE2018 and URBAN-SED datasets without requiring prior duration
knowledge. In particular, our model is capable of similar performance to
strongly-labeled supervised models on the URBAN-SED dataset. Lastly, ablation
experiments to reveal that without post-processing, our model's localization
performance drop is significantly lower compared with other approaches.


Abstract:
  The nth r-extended Lah-Bell number is defined as the number of ways a set
with $n+r$ elements can be partitioned into ordered blocks such that r
distinguished elements have to be in distinct ordered blocks. The aim of this
paper is to introduce incomplete r-extended Lah-Bell polynomials and complete
$r$-extended Lah-Bell polynomials respectively as multivariate versions of
$r$-Lah numbers and the r-extended Lah-Bell numbers and to investigate some
properties and identities for these polynomials. From these investigations, we
obtain some expressions for the r-Lah numbers and the r-extended Lah-Bell
numbers as finite sums.


Abstract:
  Conformational transitions of flexible molecules, especially those driven by
hydrophobic effects, tend to be hindered by desolvation barriers. For such
transitions, it is thus important to characterize and understand the interplay
between solvation and conformation. Using specialized molecular simulations,
here we perform such a characterization for a hydrophobic polymer solvated in
water. We find that an external potential, which unfavorably perturbs the
polymer hydration waters, can trigger a coil-to-globule or collapse transition,
and that the relative stabilities of the collapsed and extended states can be
quantified by the strength of the requisite potential. Our results also provide
mechanistic insights into the collapse transition, highlighting that polymer
collapse proceeds through the formation of a sufficiently large non-polar
cluster, and that collective water density fluctuations play an important role
in stabilizing such a cluster. We also study the collapse of the hydrophobic
polymer in octane, a non-polar solvent, and interestingly, we find that the
mechanistic details of the transition are qualitatively similar to that in
water.


Abstract:
  Motivated by the recent muon anomalous magnetic moment (g-2) measurement at
FERMILAB and non-zero neutrino masses, we propose a model based on the $SU(3)_C
\times SU(3)_L \times U(1)_X$ (3-3-1) gauge symmetry. The most popular 3-3-1
models in the literature require the presence of a scalar sextet to address
neutrino masses. In our work, we show that we can successfully implement an
one-loop linear seesaw mechanism with right-handed neutrinos, and vector-like
fermions to nicely explain the active neutrino masses, and additionally
reproduce the recent Muon g-2 result, in agreement with existing bounds.


Abstract:
  Context. In this paper we present ultra deep images of the compact group of
galaxies HCG 86 as part of the VEGAS survey. Aims. Our main goals are to
estimate the amount of intragroup light (IGL), to study the light and color
distributions in order to address the main formation process of the IGL
component in groups of galaxies. Methods. We derived the azimuthally averaged
surface brightness profiles in the g,r and i bands with g - r and r - i average
colors and color profiles for all group members. By fitting the light
distribution, we have extrapolated the contribution of the stellar halos plus
the diffuse light from the brightest component of each galaxy. The results are
compared with theoretical predictions. Results. The long integration time and
wide area covered make our data deeper than previous literature studies of the
IGL in compact groups of galaxies and allow us to produce an extended (~160
kpc) map of the IGL, down to a surface brightness level of about 30
mag/arcsec^2 in the g band. The IGL in HCG 86 is mainly in diffuse form and has
average colors of g - r ~ 0.8 mag and r - i ~ 0.4 mag. The fraction of IGL in
HCG 86 is ~ 16% of the total luminosity of the group, and this is consistent
with estimates available for other compact groups and loose groups of galaxies
of similar virial masses. A weak trend is present between the amount of IGL and
the early-type to late-type galaxy ratio. Conclusions. By comparing the IGL
fraction and colors with those predicted by simulations, the amount of IGL in
HCG 86 would be the result of the disruption of satellites at an epoch of z ~
0.4. At this redshift, observed colors are consistent with the scenario where
the main contribution to the mass of the IGL comes from the
intermediate-massive galaxies.


Abstract:
  We introduce the novel phenomena of CP-violating inflation in the frameworks
of a 3-Higgs doublet model where the inflaton doublets have a non-minimal
coupling to gravity. We allow for this coupling to be complex, thereby
introducing CP-violation - a necessary source of the baryon asymmetry - in the
inflaton couplings. We investigate the inflationary dynamics of such a
framework and the inflaton decay in the reheating phase. We discuss how the
CP-violation of the model is imprinted on the particle asymmetries.


Abstract:
  The bulk photovoltaic effect (BPVE) has attracted an increasing interest due
to its potential to overcome the efficiency limit of traditional photovoltaics,
and much effort has been devoted to understanding its underlying physics.
However, previous work has shown that theoretical models of the shift current
and the phonon-assisted ballistic current in real materials do not fully
account for the experimental BPVE photocurrent, and so other mechanisms should
be investigated in order to obtain a complete picture of BPVE. In this Letter,
we demonstrate two approaches that enable the ab initio calculation of the
ballistic current originating from the electron-hole interaction in
semiconductors. Using BaTiO$_3$ and MoS$_2$ as two examples, we show clearly
that for them the asymmetric scattering from electron-hole interaction is less
appreciable than that from electron-phonon interaction, indicating more
scattering processes need to be included to further improve the BPVE theory.
Moreover, our approaches build up a venue for predicting and designing
materials with larger ballistic current due to electron-hole interactions.


Abstract:
  Background: Testing IoT software is challenging due to large scale, volume of
data and heterogeneity. Testing automation is a much-needed feature in the
domain. Aims: The first goal of this research is to explore the requirements
and challenges of IoT testing automation. The second goal is to integrate
testing automation tools used in commercial software into the IoT context.
Method: A systematic literature review is carried out to elicit requirements
for testing automation in IoT. A design science approach is followed to build a
testing automation tool for IoT applications written in the Node-Red platform,
using the commercial testing automation tool Selenium. The resulting framework
uses the Selenium Web Driver for browser-based testing automation for IoT
applications. Results: The proposed framework has been functionally tested on
multiple browsers with preliminary evaluation on maintainability, browser
capability and comprehensiveness. Conclusions: The use of commercial tools for
testing automation in IoT is feasible. However, major challenges like high data
volumes and parallel transmission and processing of data need to be addressed
comprehensively for complete integration.


Abstract:
  Shared autonomy teleoperation can guarantee safety, but does so by reducing
the human operator's control authority, which can lead to reduced levels of
human-robot agreement and user satisfaction. This paper presents a novel haptic
shared autonomy teleoperation paradigm that uses haptic feedback to inform the
user about the inner state of a shared autonomy paradigm, while still
guaranteeing safety. This differs from haptic shared control, which uses haptic
feedback to inform the user's actions, but gives the human operator full
control over the robot's actions. We conducted a user study in which twelve
users flew a simulated UAV in a search-and-rescue task with no assistance or
assistance provided by haptic shared control, shared autonomy, or haptic shared
autonomy. All assistive teleoperation methods use control barrier functions to
find a control command that is both safe and as close as possible to the
human-generated control command. For assistive teleoperation conditions with
haptic feedback, we apply a force to the user that is proportional to the
difference between the human-generated control and the safe control. We find
that haptic shared autonomy improves the user's task performance and
satisfaction. We also find that haptic feedback in assistive teleoperation can
improve the user's situational awareness. Finally, results show that adding
haptic feedback to shared-autonomy teleoperation can improve human-robot
agreement.


Abstract:
  We consider the problem of controlling the spread of harmful items in
networks, such as the contagion proliferation of diseases or the diffusion of
fake news. We assume the linear threshold model of diffusion where each node
has a threshold that measures the node resistance to the contagion. We study
the parameterized complexity of the problem: Given a network, a set of
initially contaminated nodes, and two integers $k$ and $\ell$, is it possible
to limit the diffusion to at most $k$ other nodes of the network by immunizing
at most $\ell$ nodes? We consider several parameters associated to the input,
including: the bounds $k$ and $\ell$, the maximum node degree $\Delta$, the
treewidth, and the neighborhood diversity of the network. We first give $W[1]$
or $W[2]$-hardness results for each of the considered parameters. Then we give
fixed-parameter algorithms for some parameter combinations.


Abstract:
  We characterize the common range of the adjoints of cyclic multiplication
operators on the Drury--Arveson space. We show that a function belongs to this
common range if and only if its Taylor coefficients satisfy a simple decay
condition. To achieve this, we introduce the uniform Smirnov class on the ball
and determine its dual space. We show that the dual space of the uniform
Smirnov class equals the dual space of the strictly smaller Smirnov class of
the Drury-Arveson space, and that this in turn equals the common range of the
adjoints of cyclic multiplication operators.


Abstract:
  The voyage of the "First Fleet" from Britain to the new colony of New South
Wales was not only a military enterprise, it also had a distinct scientific
purpose. Britain's fifth Astronomer Royal, Nevil Maskelyne, had selected
William Dawes, a promising young Marine with a propensity for astronomical
observations, as his prot\'eg\'e. Maskelyne convinced the British Board of
Longitude to supply Dawes with a suite of state-of-the-art instruments and
allow the young Marine to establish an observatory in the new settlement. The
Astronomer Royal may have had a dual motivation, one driven by strategic
national interests combined with a personal investment linked to the suggested
re-appearance of a comet in the southern sky. With the unexpected assistance of
the French Lap\'erouse expedition, between 1788 and 1791 Dawes established not
one but two observatories within a kilometre of Sydney's present-day city
centre. Motivated by persisting confusion in the literature, we explore the
historical record to narrow down the precise location of Dawes' observatory. We
conclude that the memorial plaque attached to Sydney Harbour Bridge indicates
an incorrect location. Overwhelming contemporary evidence -- maps, charts and
pictorial representations -- implies that Dawes' observatory was located on the
northeastern tip of the promontory presently known as The Rocks (formerly
Dawes' Point), with any remains having vanished during the construction of the
Harbour Bridge.


Abstract:
  Nuclear Magnetic Ressonance (NMR) is a widely used technique, with a long
history of applications in chemestry, medicine, and material science. Twenty
years ago, it emerged as a reliable source for quantum computing too, since the
work of Cory. One of its major advantage is the ease with which arbitrary
unitary transformations can be implemented, together with its experimental
simplicity, that can be traced back to very simple NMR routines, which were
being extensively used long before. However, some disadvantages came along,
mostly related to experimental effort in the initialisation and measure
processes, and scalability. In this work, we have successfully probed some
simple quantum gates (Pauli-Z, Pauli-X and Hadamard) in one and two-qubit
systems, realised in a NMR experiment. The work comprised a pseudo-pure state
preparation, followed by the application of the gates, and a quantum tomography
method, necessary to reconstruct the density matrix. The experiments were
conducted with a chloroform sample, placed in a 300 MHz Bruker Avance II
spectrometer, equipped with a superconducting magnet with a 7 T magnetic field.


Abstract:
  Data privacy has become an increasingly important issue in Machine Learning
(ML), where many approaches have been developed to tackle this challenge, e.g.
cryptography (Homomorphic Encryption (HE), Differential Privacy (DP), etc.) and
collaborative training (Secure Multi-Party Computation (MPC), Distributed
Learning and Federated Learning (FL)). These techniques have a particular focus
on data encryption or secure local computation. They transfer the intermediate
information to the third party to compute the final result. Gradient exchanging
is commonly considered to be a secure way of training a robust model
collaboratively in Deep Learning (DL). However, recent researches have
demonstrated that sensitive information can be recovered from the shared
gradient. Generative Adversarial Network (GAN), in particular, has shown to be
effective in recovering such information. However, GAN based techniques require
additional information, such as class labels which are generally unavailable
for privacy-preserved learning. In this paper, we show that, in the FL system,
image-based privacy data can be easily recovered in full from the shared
gradient only via our proposed Generative Regression Neural Network (GRNN). We
formulate the attack to be a regression problem and optimize two branches of
the generative model by minimizing the distance between gradients. We evaluate
our method on several image classification tasks. The results illustrate that
our proposed GRNN outperforms state-of-the-art methods with better stability,
stronger robustness, and higher accuracy. It also has no convergence
requirement to the global FL model. Moreover, we demonstrate information
leakage using face re-identification. Some defense strategies are also
discussed in this work.


Abstract:
  We study the dimension theory of limit sets of iterated function systems
consisting of a countably infinite number of contractions. Our primary focus is
on the intermediate dimensions: a family of dimensions depending on a parameter
$\theta \in [0,1]$ which interpolate between the Hausdorff and box dimensions.
Our main results are in the case when all the contractions are conformal. Under
a natural separation condition we prove that the intermediate dimensions of the
limit set are the maximum of the Hausdorff dimension of the limit set and the
intermediate dimensions of the set of fixed points of the contractions. This
builds on work of Mauldin and Urba\'nski concerning the Hausdorff and upper box
dimension. We give several (often counter-intuitive) applications of our work
to dimensions of projections, fractional Brownian images, and general H\"older
images. These applications apply to well-studied examples such as sets of
numbers which have real or complex continued fraction expansions with
restricted entries.
  We also obtain several results without assuming conformality or any
separation conditions. We prove general upper bounds for the Hausdorff, box and
intermediate dimensions of infinitely generated attractors in terms of a
topological pressure function. We also show that the limit set of a 'generic'
infinite iterated function system has box and intermediate dimensions equal to
the ambient spatial dimension, where 'generic' can refer to any one of (i) full
measure; (ii) prevalent; or (iii) comeagre.


Abstract:
  Non-trivial spin structures in itinerant magnets can give rise to topological
Hall effect (THE) due to the interacting local magnetic moments and conductive
electrons. While, in series of materials, THE has mostly been observed at low
temperatures far below room temperature (RT) limiting its potential
applications. Here, we report the anisotropic anomalous Hall effect (AHE) near
RT in LaMn2Ge2, a noncollinear ferromagnetic (FM) with Curie temperature TC=325
K. Large topological Hall resistivity of ~1.0 10-6 ohmcm in broad temperature
range (190 K <T< 300 K) is realized as field (H) parallel to the ab-plane (H //
ab) and current along c axis (I // c), in contrast to the conventional AHE for
H // c and I // ab. The emergence of THE is attributed to the spin chirality of
noncoplanar spin configurations stabilized by thermal fluctuation during spin
flop process. Moreover, the constructed temperature-field (H-T) phase diagrams
based on the isothermal topological Hall resistivity reveal a field-induced
transition from the noncoplanar spin configuration to polarized ferromagnetic
states. Our experimental realization of large THE near RT highlights LaMn2Ge2
as a promising system for functional applications in novel spintronic devices.


Abstract:
  We revisit the calculation of the fermion self-energy in QED in the presence
of a magnetic field. We show that, after carrying out the renormalization
procedure and identifying the most general perturbative tensor structure for
the modified fermion {mass operator} in the large field limit, the mass
develops an imaginary part. This happens when account is made of the
sub-leading contributions associated to Landau levels other than the lowest
one. The imaginary part is associated to a spectral density describing the
spread of the mass function in momentum. The center of the distribution
corresponds to the magnetic-field modified mass. The width becomes small as the
field intensity increases in such a way that for asymptotically large values of
the field, when the separation between Landau levels becomes also large, the
mass function describes a stable particle occupying only the lowest Landau
level. For large but finite values of the magnetic field, the spectral density
represents a finite probability for the fermion to occupy Landau levels other
than the LLL.


Abstract:
  This paper revisits the discussion on determinants of budget balances and
investigates the change in their effect in light of the COVID-19 crisis by
utilizing data on 43 countries and a system generalized method of moments
approach. The results show that the overall impact of the global pandemic led
to a disproportionate increase in the magnitude of the estimated effects of the
macroeconomic determinants on the budget balance. However, we also find that
more developed economies were able to undertake higher stimulus packages for
the relatively same level of primary balance. We believe that one of the
factors affecting this outcome is that that they exhibit a higher government
debt position in domestic currency denomination.


Abstract:
  By applying the formula for essential Whittaker functions established by
Matringe and Miyauchi, we study five integral representations for irreducible
admissible generic representations of ${\rm GL}_n$ over $p$-adic fields. In
each case, we show that the integrals achieve local formal $L$-functions
defined by Langlands parameters, when the test vector is associated to the
newform. We give the relation between local periods involving essential
Whittaker functions and special values of formal $L$-factors at $s=1$ for
certain distinguished or unitary representations. The period integrals are also
served as standard non-zero distinguished forms.


Abstract:
  Proportional ranking rules aggregate approval-style preferences of agents
into a collective ranking such that groups of agents with similar preferences
are adequately represented. Motivated by the application of live Q&A platforms,
where submitted questions need to be ranked based on the interests of the
audience, we study a dynamic extension of the proportional rankings setting. In
our setting, the goal is to maintain the proportionality of a ranking when
alternatives (i.e., questions) -- not necessarily from the top of the ranking
-- get selected sequentially. We propose generalizations of well-known
aggregation rules to this setting and study their monotonicity and
proportionality properties. We also evaluate the performance of these rules
experimentally, using realistic probabilistic assumptions on the selection
procedure.


Abstract:
  We introduce and evaluate the degree of convexity of an unit ball, so-called,
characteristic of convexity (COC) for the Grand Lebesgue Spaces, (GLS), which
is a slight analog of the classical notion of the modulus of convexity (MOC).


Abstract:
  This paper proposes an algorithmic method to heuristically solve the famous
Travelling Salesman Problem (TSP) when the salesman's path evolves in
continuous state space and discrete time but with otherwise arbitrary
(nonlinear) dynamics. The presented method is based on the framework of
Symbolic Control. In this way, our method returns a provably correct
state-feedback controller for the underlying coverage specification, which is
the TSP leaving out the requirement for optimality on the route. In addition,
we utilize the Lin-Kernighan-Helsgaun TSP solver to heuristically optimize the
cost for the overall taken route. Two examples, an urban parcel delivery task
and a UAV reconnaissance mission, greatly illustrate the powerfulness of the
proposed heuristic.


Abstract:
  In this paper we provide the basic setup for a project, initiated by Felix
Rehren, aiming at classifying all $2$-generated primitive axial agebras of
Monster type $(\alpha, \beta)$. We first revise Rehren's construction of an
initial object in the cathegory of primitive $n$-generated axial algebras
giving a formal one, filling some gaps and, though correcting some
inaccuracies, confirm Rehren's results. Then we focus on $2$-generated algebras
which naturally part into three cases: the two critical cases $\alpha=2\beta$
and $\alpha=4\beta$, and the generic case (i.e. all the rest). About these
cases, which will be dealt in detail in subsequent papers, we give bounds on
the dimensions (the generic case already treated by Rehen) and classify all
2-generated primitive axial algebras of Monster type $(\alpha, \beta)$ over
$\mathbb Q(\alpha, \beta)$ for $\alpha$ and $\beta$ algebraically independent
indeterminates over $\mathbb Q$. Finally we restrict to the $2$-generated
Majorana algebras (i.e. when $\alpha=\frac{1}{4}$ and $\beta=\frac{1}{32}$),
showing that these fall precisely into the nine isomorphism types of the
Norton-Sakuma algebras.


Abstract:
  We present an ensemble X-ray analysis of systematic perturbations in the
central hot gas properties for a sample of 28 nearby strong cool-core systems
selected from the HIghest X-ray FLUx Galaxy Cluster Sample (HIFLUGCS). We
analyze their cool-core features observed with the Chandra X-ray Observatory.
All individual systems in our sample exhibit at least a pair of positive and
negative excess perturbations in the X-ray residual image after subtracting the
global brightness profile. We extract and analyze X-ray spectra of the
intracluster medium (ICM) in the detected perturbed regions. To investigate
possible origins of the gas perturbations, we characterize thermodynamic
properties of the ICM in the perturbed regions and characterize their
correlations between positive and negative excess regions. The best-fit
relations for temperature and entropy show a clear offset from the one-to-one
relation, $T_\mathrm{neg}/T_\mathrm{pos}=1.20^{+0.04}_{-0.03}$ and
$K_\mathrm{neg}/K_\mathrm{pos}=1.43\pm 0.07$, whereas the best-fit relation for
pressure is found to be remarkably consistent with the one-to-one relation
$P_\mathrm{neg}=P_\mathrm{pos}$, indicating that the ICM in the perturbed
regions is in pressure equilibrium. These observed features in the HIFLUGCS
sample are in agreement with the hypothesis that the gas perturbations in cool
cores are generated by gas sloshing. We also analyze synthetic observations of
perturbed cluster cores created from binary merger simulations, finding that
the observed temperature ratio agrees with the simulations,
$T_\mathrm{neg}/T_\mathrm{pos}\sim 1.3$. We conclude that gas sloshing induced
by infalling substructures plays a major role in producing the characteristic
gas perturbations in cool cores. The ubiquitous presence of gas perturbations
in cool cores may suggest a significant contribution of gas sloshing to
suppressing runaway cooling of the ICM.


Abstract:
  The past two decades have witnessed the great success of the algorithmic
modeling framework advocated by Breiman et al. (2001). Nevertheless, the
excellent prediction performance of these black-box models rely heavily on the
availability of strong supervision, i.e. a large set of accurate and exact
ground-truth labels. In practice, strong supervision can be unavailable or
expensive, which calls for modeling techniques under weak supervision. In this
comment, we summarize the key concepts in weakly supervised learning and
discuss some recent developments in the field. Using algorithmic modeling alone
under a weak supervision might lead to unstable and misleading results. A
promising direction would be integrating the data modeling culture into such a
framework.


Abstract:
  We introduce a notion of scalar curvature of a twisted generalized Kahler
manifold in terms of pure spinors formalism. A moment map framework with a
modified action of generalized Hamiltonians on an arbitrary compact generalized
Kahler manifold is developed. Then it turns out that a moment map is given by
the scalar curvature, which is a generalization of the result of the scalar
curvature as a moment map in the ordinary Kahler geometry, due to Fujiki and
Donaldson.
  A noncommutative compact Lie group $G$ does not have any Kahler structure.
However, we show that every compact Lie group admits generalized Kahler
structures with constant scalar curvature. In particular, generalized Kahler
structures with constant scalar curvature on the standard Hopf surface are
explicitly given.


Abstract:
  Lithium-salt-based deep eutectic solvents, where the only cation is Li+, are
promising candidates as electrolytes in electrochemical energy-storage devices
like batteries. We have performed broadband dielectric spectroscopy on three
such systems, covering a broad temperature and dynamic range that extends from
the low-viscosity liquid around room temperature down to the glassy state
approaching the glass-transition temperature. We detect a relaxational process
that can be ascribed to dipolar reorientational dynamics and exhibits the clear
signatures of glassy freezing. We find that the temperature dependence of the
ionic dc conductivity and its room-temperature value also are governed by the
glassy dynamics of these systems, depending, e.g., on the glass-transition
temperature and fragility. Compared to the previously investigated
corresponding systems, containing choline chloride instead of a lithium salt,
both the reorientational and ionic dynamics are significantly reduced due to
variations of the glass-transition temperature and the higher ionic potential
of the lithium ions. These lithium-based deep eutectic solvents partly exhibit
significant decoupling of the dipolar reorientational and the ionic
translational dynamics and approximately follow a fractional
Debye-Stokes-Einstein relation, leading to an enhancement of the dc
conductivity, especially at low temperatures. The presented results clearly
reveal the importance of decoupling effects and of the typical glass-forming
properties of these systems for the technically relevant room-temperature
conductivity.


Abstract:
  We give a new proof of a partition theorem popularly known as Elder's
theorem, but which is also credited to Stanley and Fine. We extend the theorem
to the context of colored partitions (or prefabs). More specifically, we give
analogous results for $b$-colored partitions, where each part occurs in $b$
colors; for $b$-colored partitions with odd parts (or distinct parts); for
partitions where the part $k$ comes in $k$ colors; and, overpartitions.


Abstract:
  Optical microresonators are of paramount importance in photonic circuits
requiring fine spectral filtering or resonant light recirculation. Key
performance metrics improve with increasing resonance quality factor (Q) across
all applications. The performance of silicon photonic circuits is often
hampered by the low-quality factor of planar silicon microresonators, typically
of Q~10^4-10^5. On the other hand, bulk whispering gallery mode resonators
provide a wide range of materials with intriguing optical properties and
exceptionally high resonant quality factors Q>10^7. However, the efficient
coupling between bulk resonators and planar Si photonic waveguides is
considered challenging, if not impossible, due to remarkably large mismatch in
size and refractive index. Here, we show an efficient method to couple bulk
resonators and Si waveguides based on subwavelength metamaterial engineering of
silicon. Based on this approach, we experimentally demonstrate coupling between
220-nm-thick Si waveguides and bulk microresonators made of silica, lithium
niobate and calcium fluoride with diameters in the 0.3-3.5 mm range, achieving
high coupling efficiency of 75-99% and exceptional Q of 10^6-10^7. These
results open a new route for the heterogeneous integration of bulk resonators
and silicon photonic circuits, with great potential for applications in
sensing, microwave-photonics, and quantum photonics, to name a few.


Abstract:
  The emergence of CNNs in mainstream deployment has necessitated methods to
design and train efficient architectures tailored to maximize the accuracy
under diverse hardware & latency constraints. To scale these resource-intensive
tasks with an increasing number of deployment targets, Once-For-All (OFA)
proposed an approach to jointly train several models at once with a constant
training cost. However, this cost remains as high as 40-50 GPU days and also
suffers from a combinatorial explosion of sub-optimal model configurations. We
seek to reduce this search space -- and hence the training budget -- by
constraining search to models close to the accuracy-latency Pareto frontier. We
incorporate insights of compound relationships between model dimensions to
build CompOFA, a design space smaller by several orders of magnitude. Through
experiments on ImageNet, we demonstrate that even with simple heuristics we can
achieve a 2x reduction in training time and 216x speedup in model
search/extraction time compared to the state of the art, without loss of Pareto
optimality! We also show that this smaller design space is dense enough to
support equally accurate models for a similar diversity of hardware and latency
targets, while also reducing the complexity of the training and subsequent
extraction algorithms.


Abstract:
  We demonstrate the use of a single trapped ion as a sensor to probe
electric-field noise from interchangeable test surfaces. As proof of principle,
we measure the magnitude and distance dependence of electric-field noise from
two ion-trap-like samples with patterned Au electrodes. This trapped-ion sensor
could be combined with other surface characterization tools to help elucidate
the mechanisms that give rise to electric-field noise from ion-trap surfaces.
Such noise presents a significant hurdle for performing large-scale trapped-ion
quantum computations.


Abstract:
  In several examples it has been observed that a module category of a vertex
operator algebra (VOA) is equivalent to a category of representations of some
quantum group. The present article is concerned with developing such a duality
in the case of the Virasoro VOA at generic central charge; arguably the most
rudimentary of all VOAs, yet structurally complicated. We do not address the
category of all modules of the generic Virasoro VOA, but we consider the
infinitely many modules from the first row of the Kac table. Building on an
explicit quantum group method of Coulomb gas integrals, we give a new proof of
the fusion rules, we prove the analyticity of compositions of intertwining
operators, and we show that the conformal blocks are fully determined by the
quantum group method. Crucially, we prove the associativity of the intertwining
operators among the first-row modules, and find that the associativity is
governed by the $6j$-symbols of the quantum group. Our results constitute a
concrete duality between a VOA and a quantum group, and they will serve as the
key tools to establish the equivalence of the first-row subcategory of modules
of the generic Virasoro VOA and the category of (type-1) finite-dimensional
representations of $\mathcal{U}_{q}(\mathfrak{sl}_{2})$.


Abstract:
  The first evidence for X(3872) production in relativistic heavy ion
collisions is reported. The X(3872) production is studied in lead-lead (PbPb)
collisions at a center-of-mass energy of $\sqrt{s_\mathrm{NN}}=$ 5.02 TeV per
nucleon pair, using the decay chain X(3872) $\to$ J$/\psi\, \pi^+\pi^- \to
\mu^+\mu^-\pi^+\pi^-$. The data were recorded with the CMS detector in 2018 and
correspond to an integrated luminosity of 1.7 nb$^{-1}$. The measurement is
performed in the rapidity and transverse momentum ranges $|y|\lt$ 1.6 and 15
$\lt p_\mathrm{T} \lt$ 50 GeV$/c$. The significance of the inclusive X(3872)
signal is 4.2 standard deviations. The prompt X(3872) to $\psi$(2S) yield ratio
is found to be $\rho^\mathrm{PbPb} = $ 1.08 $\pm$ 0.49 (stat) $\pm$ 0.52
(syst), to be compared with typical values of 0.1 for pp collisions. This
result provides a unique experimental input to theoretical models of the
X(3872) production mechanism, and of the nature of this exotic state.


Abstract:
  In most websites, the online rating system provides the ratings of products
and services to users. Lack of trust in data integrity and its manipulation has
hindered fulfilling user satisfaction. Since existing online rating systems
deal with a central server, all rating data is stored on the central server.
Therefore, all rating data can be removed, modified, and manipulated by the
system manager to change the ratings in favor of the service or product
provider. In this paper, an online rating system using distributed ledger
technologies has been presented as the proposed system to solve all the
weaknesses of current systems. Distributed ledger technologies are completely
decentralized and there is no centralization on them by any institution.
Distributed ledger technologies have different variants. Among distributed
ledger technologies, blockchain technology has been used in the proposed rating
system because of its support for smart contracts. In the proposed online
rating system, the Ethereum platform has been chosen from different blockchain
platforms that have a public permission network. In this system, the raters
cannot rate unless they submit a request to the system and be authorized to
take part in the online product rating process. The important feature of the
Ethereum platform is its support for smart contracts, which can be used to
write the rating contract in the Solidity language. Also, using Proof of
Authority consensus mechanisms, all rating transactions are approved by the
surveyors. Since in the real Ethereum system, each rating transaction is sent
to the network by the raters, some gas must be paid for each rating
transaction. However, since this method is expensive, TestNet blockchain can be
used in the rating system. Finally, the proposed rating system was used for
rating the restaurants of a website and its features were tested.


Abstract:
  We report on Bayesian estimation of the radius, mass, and hot surface regions
of the massive millisecond pulsar PSR J0740$+$6620, conditional on
pulse-profile modeling of Neutron Star Interior Composition Explorer X-ray
Timing Instrument (NICER XTI) event data. We condition on informative pulsar
mass, distance, and orbital inclination priors derived from the joint NANOGrav
and CHIME/Pulsar wideband radio timing measurements of arXiv:2104.00880. We use
XMM European Photon Imaging Camera spectroscopic event data to inform our X-ray
likelihood function. The prior support of the pulsar radius is truncated at 16
km to ensure coverage of current dense matter models. We assume conservative
priors on instrument calibration uncertainty. We constrain the equatorial
radius and mass of PSR J0740$+$6620 to be $12.39_{-0.98}^{+1.30}$ km and
$2.072_{-0.066}^{+0.067}$ M$_{\odot}$ respectively, each reported as the
posterior credible interval bounded by the 16% and 84% quantiles, conditional
on surface hot regions that are non-overlapping spherical caps of fully-ionized
hydrogen atmosphere with uniform effective temperature; a posteriori, the
temperature is $\log_{10}(T$ [K]$)=5.99_{-0.06}^{+0.05}$ for each hot region.
All software for the X-ray modeling framework is open-source and all data,
model, and sample information is publicly available, including analysis
notebooks and model modules in the Python language. Our marginal likelihood
function of mass and equatorial radius is proportional to the marginal joint
posterior density of those parameters (within the prior support) and can thus
be computed from the posterior samples.


Abstract:
  Monolayer graphene aligned with hexagonal boron nitride (hBN) develops a gap
at the charge neutrality point (CNP). This gap has previously been extensively
studied by electrical transport through thermal activation measurements. Here,
we report the determination of the gap size at the CNP of graphene/hBN
superlattice through photocurrent spectroscopy study. We demonstrate two
distinct measurement approaches to extract the gap size. A maximum of ~ 14 meV
gap is observed for devices with a twist angle of less than 1 degree. This
value is significantly smaller than that obtained from thermal activation
measurements, yet larger than the theoretically predicted single-particle gap.
Our results suggest that lattice relaxation and moderate electron-electron
interaction effects may enhance the CNP gap in hBN/graphene superlattice.


Abstract:
  We consider a parallel server system with so-called cancel-on-completion
redundancy. There are $n$ servers and multiple job classes $j$. An arriving
class $j$ job consists of $d_j$ components, placed on a randomly selected
subset of servers; the job service is complete as soon as $k_j$ components out
of $d_j$ complete their service, at which point the service of all remaining
$d_j-k_j$ components is canceled. The system is in general non-work-conserving
-- the average amount of new workload added to the system by an arriving class
$j$ job depends on the system state. This poses the main challenge for the
system analysis.
  The results of this paper concern both the system with fixed number of
servers $n$ and the mean-field asymptotic regime when $n\to\infty$ while each
job class arrival rate per server remains constant. The main question we
address for the asymptotic regime is whether the steady-state asymptotic
independence of server workloads holds. We prove that this property does hold
under certain conditions, including the important special case when job
components of each class $j$ are i.i.d. with an increasing-hazard-rate
distribution.


Abstract:
  In this work, a hybrid integration of few-layer transition metal
dichalcogenides (TMDCs) and ferroelectric polymer is designed to achieve
passive control of optical properties in-situ. The electrical polarization in
ferroelectric P(VDF-TrFE) polymer can regulate the photoluminescence (PL) in
few-layer TMDCs. The total PL intensity is substantially suppressed or enhanced
under opposite polarization in bilayer WSe2. This is because electrons transfer
between valley K and {\Lambda} in the conduction band induced by the built-in
electric field in P(VDF-TrFE) polymer. This charge transfer further changes the
competing dynamics between direct and indirect exciton recombination path and
overall optical radiation efficiency. We also illustrate that the engineered PL
originates from external electric field dependent transferred electron effect.
The theoretical result matches the experimental data well. This work
demonstrates a device platform in which passive regulation is achieved using 2D
TMDCs modulated by polarized ferroelectric materials.


Abstract:
  Inertial Measurement Unit (IMU) sensors are present in everyday devices such
as smartphones and fitness watches. As a result, the array of health-related
research and applications that tap onto this data has been growing, but little
attention has been devoted to the prediction of an individual's heart rate (HR)
from IMU data, when undergoing a physical activity. Would that be even
possible? If so, this could be used to design personalized sets of aerobic
exercises, for instance. In this work, we show that it is viable to obtain
accurate HR predictions from IMU data using Recurrent Neural Networks, provided
only access to HR and IMU data from a short-lived, previously executed
activity. We propose a novel method for initializing an RNN's hidden state
vectors, using a specialized network that attempts to extract an embedding of
the physical conditioning (PCE) of a subject. We show that using a
discriminator in the training phase to help the model learn whether two PCEs
belong to the same individual further reduces the prediction error. We evaluate
the proposed model when predicting the HR of 23 subjects performing a variety
of physical activities from IMU data available in public datasets (PAMAP2,
PPG-DaLiA). For comparison, we use as baselines the only model specifically
proposed for this task and an adapted state-of-the-art model for Human Activity
Recognition (HAR), a closely related task. Our method, PCE-LSTM, yields over
10% lower mean absolute error. We demonstrate empirically that this error
reduction is in part due to the use of the PCE. Last, we use the two datasets
(PPG-DaLiA, WESAD) to show that PCE-LSTM can also be successfully applied when
photoplethysmography (PPG) sensors are available, outperforming the
state-of-the-art deep learning baselines by more than 30%.


Abstract:
  For $X$ a smooth projective variety and $D=D_1+\ldots+D_n$ a simple normal
crossings divisor, we establish a precise cycle-level correspondence between
the genus zero local Gromov-Witten theory of the bundle $\oplus_{i=1}^n
\mathcal{O}_X(-D_i)$ and the maximal contact Gromov-Witten theory of the
multi-root stack $X_{D,\vec{r}}$. The proof is an implementation of the rank
reduction strategy. We use this point of view to clarify the relationship
between logarithmic and orbifold invariants.


Abstract:
  The Best Estimate plus Uncertainty (BEPU) approach for nuclear systems
modeling and simulation requires that the prediction uncertainty must be
quantified in order to prove that the investigated design stays within
acceptance criteria. A rigorous Uncertainty Quantification (UQ) process should
simultaneously consider multiple sources of quantifiable uncertainties: (1)
parameter uncertainty due to randomness or lack of knowledge; (2) experimental
uncertainty due to measurement noise; (3) model uncertainty caused by
missing/incomplete physics and numerical approximation errors, and (4) code
uncertainty when surrogate models are used. In this paper, we propose a
comprehensive framework to integrate results from inverse UQ and quantitative
validation to provide robust predictions so that all these sources of
uncertainties can be taken into consideration.
  Inverse UQ quantifies the parameter uncertainties based on experimental data
while taking into account uncertainties from model, code and measurement. In
the validation step, we use a quantitative validation metric based on Bayesian
hypothesis testing. The resulting metric, called the Bayes factor, is then used
to form weighting factors to combine the prior and posterior knowledge of the
parameter uncertainties in a Bayesian model averaging process. In this way,
model predictions will be able to integrate the results from inverse UQ and
validation to account for all available sources of uncertainties. This
framework is a step towards addressing the ANS Nuclear Grand Challenge on
"Simulation/Experimentation" by bridging the gap between models and data.


Abstract:
  This letter studies the problem of minimizing increasing set functions, or
equivalently, maximizing decreasing set functions, over the base of a matroid.
This setting has received great interest, since it generalizes several applied
problems including actuator and sensor placement problems in control theory,
multi-robot task allocation problems, video summarization, and many others. We
study two greedy heuristics, namely, the forward and the reverse greedy
algorithms. We provide two novel performance guarantees for the approximate
solutions obtained by these heuristics depending on both the submodularity
ratio and the curvature.


Abstract:
  This article investigates why the genus two, supermoduli space of curves will
split in contrast to, potentially, almost all other supermoduli spaces. We use
that the dimension of the odd, versal deformation space of a genus two, super
Riemann surface is two dimensional. As a consequence, the odd versal
deformations can be generated by Schiffer variations at the associated points
of a Szego kernel. This idea is present in D'Hoker and Phong's two loop,
superstring amplitude calculation. We show how this idea, combined with Donagi
and Witten's characterization of supermoduli obstructions, will result in a
splitting of supermoduli space in genus two.


Abstract:
  In tasks such as tracking, time-series data inevitably carry missing
observations. While traditional tracking approaches can handle missing
observations, recurrent neural networks (RNNs) are designed to receive input
data in every step. Furthermore, current solutions for RNNs, like omitting the
missing data or data imputation, are not sufficient to account for the
resulting increased uncertainty. Towards this end, this paper introduces an
RNN-based approach that provides a full temporal filtering cycle for motion
state estimation. The Kalman filter inspired approach, enables to deal with
missing observations and outliers. For providing a full temporal filtering
cycle, a basic RNN is extended to take observations and the associated belief
about its accuracy into account for updating the current state. An RNN
prediction model, which generates a parametrized distribution to capture the
predicted states, is combined with an RNN update model, which relies on the
prediction model output and the current observation. By providing the model
with masking information, binary-encoded missing events, the model can overcome
limitations of standard techniques for dealing with missing input values. The
model abilities are demonstrated on synthetic data reflecting prototypical
pedestrian tracking scenarios.


Abstract:
  Biological systems perceive the world by simultaneously processing
high-dimensional inputs from modalities as diverse as vision, audition, touch,
proprioception, etc. The perception models used in deep learning on the other
hand are designed for individual modalities, often relying on domain-specific
assumptions such as the local grid structures exploited by virtually all
existing vision models. These priors introduce helpful inductive biases, but
also lock models to individual modalities. In this paper we introduce the
Perceiver - a model that builds upon Transformers and hence makes few
architectural assumptions about the relationship between its inputs, but that
also scales to hundreds of thousands of inputs, like ConvNets. The model
leverages an asymmetric attention mechanism to iteratively distill inputs into
a tight latent bottleneck, allowing it to scale to handle very large inputs. We
show that this architecture is competitive with or outperforms strong,
specialized models on classification tasks across various modalities: images,
point clouds, audio, video, and video+audio. The Perceiver obtains performance
comparable to ResNet-50 and ViT on ImageNet without 2D convolutions by directly
attending to 50,000 pixels. It is also competitive in all modalities in
AudioSet.


Abstract:
  Masked Language Models (MLMs) have shown superior performances in numerous
downstream NLP tasks when used as text encoders. Unfortunately, MLMs also
demonstrate significantly worrying levels of social biases. We show that the
previously proposed evaluation metrics for quantifying the social biases in
MLMs are problematic due to following reasons: (1) prediction accuracy of the
masked tokens itself tend to be low in some MLMs, which raises questions
regarding the reliability of the evaluation metrics that use the (pseudo)
likelihood of the predicted tokens, and (2) the correlation between the
prediction accuracy of the mask and the performance in downstream NLP tasks is
not taken into consideration, and (3) high frequency words in the training data
are masked more often, introducing noise due to this selection bias in the test
cases. To overcome the above-mentioned disfluencies, we propose All Unmasked
Likelihood (AUL), a bias evaluation measure that predicts all tokens in a test
case given the MLM embedding of the unmasked input. We find that AUL accurately
detects different types of biases in MLMs. We also propose AUL with attention
weights (AULA) to evaluate tokens based on their importance in a sentence.
However, unlike AUL and AULA, previously proposed bias evaluation measures for
MLMs systematically overestimate the measured biases, and are heavily
influenced by the unmasked tokens in the context.


Abstract:
  Energy storage technologies that can meet the unprecedented demands of a
sustainable energy system based on intermittent energy sources require new
battery materials. We investigate high ionic conductors, Li$_3$ClO glasses. In
the present work we use a first principles method to model the amorphous
structure of the glass. We characterize the structure by means of radial
distribution functions, radial disctributions functions and coordination
numbers. We compare to their crystalline counterparts. The electronic structure
of the glass is compared to that of the crystalline material. The band gap of
the glass appears to be slighly reduced compared to that of the crystal. We
also investigate the chemical stability of the glass against Li metal
electrode. The electrochemical stability of the glassy electrolite is evaluated
against Li metal.


Abstract:
  Steady plasma flows have been studied almost exclusively in systems with
continuous symmetry or in open domains. In the absence of continuous symmetry,
the lack of a conserved quantity makes the study of flows intrinsically
challenging. In a toroidal domain, the requirement of double-periodicity for
physical quantities adds to the complications. In particular, the
magnetohydrodynamics (MHD) model of plasma steady-state with the flow in a
non-symmetric toroidal domain allows the development of singularities when the
rotational transform of the magnetic field is rational, much like the
equilibrium MHD model. In this work, we show that steady flows can still be
maintained provided the rotational transform is close to rational and the
magnetic shear is weak. We extend the techniques developed in carrying out
perturbation methods to all orders for static MHD equilibrium by Weitzner
(Physics of Plasmas 21, 022515 (2014)) to MHD equilibrium with flows. We
construct perturbative MHD equilibrium in a doubly-periodic domain with nearly
parallel flows by systematically eliminating magnetic resonances order by
order. We then utilize an additional symmetry of the flow problem, first
discussed by E. Hameiri in (J. Math. Phys. \textbf{22}, 2080 (1981) Sec. III),
to obtain a generalized Grad-Shafranov equation for a class of non-symmetric
three-dimensional MHD equilibrium with flows both parallel and perpendicular to
the magnetic field. For this class of flows, we are able to obtain
non-symmetric generalizations of integrals of motion, such as Bernoulli's
function and angular momentum. Finally, we obtain the generalized Hamada
conditions, which are consistency conditions necessary to suppress singular
currents in such a system when the magnetic field lines are closed. We do not
attempt to address the question of neoclassical damping of flows.


Abstract:
  Reliable automation of the labor-intensive manual task of scoring animal
sleep can facilitate the analysis of long-term sleep studies. In recent years,
deep-learning-based systems, which learn optimal features from the data,
increased scoring accuracies for the classical sleep stages of Wake, REM, and
Non-REM. Meanwhile, it has been recognized that the statistics of transitional
stages such as pre-REM, found between Non-REM and REM, may hold additional
insight into the physiology of sleep and are now under vivid investigation. We
propose a classification system based on a simple neural network architecture
that scores the classical stages as well as pre-REM sleep in mice. When
restricted to the classical stages, the optimized network showed
state-of-the-art classification performance with an out-of-sample F1 score of
0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1
scores on pre-REM (0.5) compared to the classical stages. The result is
comparable to previous attempts to score transitional stages in other species
such as transition sleep in rats or N1 sleep in humans. Nevertheless, we
observed that the sequence of predictions including pre-REM typically
transitioned from Non-REM to REM reflecting sleep dynamics observed by human
scorers. Our findings provide further evidence for the difficulty of scoring
transitional sleep stages, likely because such stages of sleep are
under-represented in typical data sets or show large inter-scorer variability.
We further provide our source code and an online platform to run predictions
with our trained network.


Abstract:
  While large self-supervised models have rivalled the performance of their
supervised counterparts, small models still struggle. In this report, we
explore simple baselines for improving small self-supervised models via
distillation, called SimDis. Specifically, we present an offline-distillation
baseline, which establishes a new state-of-the-art, and an online-distillation
baseline, which achieves similar performance with minimal computational
overhead. We hope these baselines will provide useful experience for relevant
future research. Code is available at: https://github.com/JindongGu/SimDis/


Abstract:
  In this paper, we construct defects (domain walls) that connect different
phases of two-dimensional gauged linear sigma models (GLSMs), as well as
defects that embed those phases into the GLSMs. Via their action on boundary
conditions these defects give rise to functors between the D-brane categories,
which respectively describe the transport of D-branes between different phases,
and embed the D-brane categories of the phases into the category of D-branes of
the GLSMs.


Abstract:
  Traditional Cyber-physical Systems(CPSs) were not built with cybersecurity in
mind. They operated on separate Operational Technology (OT) networks. As these
systems now become more integrated with Information Technology (IT) networks
based on IP, they expose vulnerabilities that can be exploited by the attackers
through these IT networks. The attackers can control such systems and cause
behavior that jeopardizes the performance and safety measures that were
originally designed into the system. In this paper, we explore the approaches
to identify threats to CPSs and ensure the quality of the created threat
models. The study involves interviews with eleven security experts working in
security consultation companies, software engineering companies, an Original
Equipment Manufacturer (OEM),and ground and areal vehicles integrators. We
found through these interviews that the practitioners use a combination of
various threat modeling methods, approaches, and standards together when they
perform threat modeling of given CPSs. key challenges practitioners face are:
they cannot transfer the threat modeling knowledge that they acquire in a
cyber-physical domain to other domains, threat models of modified systems are
often not updated, and the reliance on mostly peer-evaluation and quality
checklists to ensure the quality of threat models. The study warns about the
difficulty to develop secure CPSs and calls for research on developing
practical threat modeling methods for CPSs, techniques for continuous threat
modeling, and techniques to ensure the quality of threat models.


Abstract:
  Modality is the linguistic ability to describe events with added information
such as how desirable, plausible, or feasible they are. Modality is important
for many NLP downstream tasks such as the detection of hedging, uncertainty,
speculation, and more. Previous studies that address modality detection in NLP
often restrict modal expressions to a closed syntactic class, and the modal
sense labels are vastly different across different studies, lacking an accepted
standard. Furthermore, these senses are often analyzed independently of the
events that they modify. This work builds on the theoretical foundations of the
Georgetown Gradable Modal Expressions (GME) work by Rubinstein et al. (2013) to
propose an event-based modality detection task where modal expressions can be
words of any syntactic class and sense labels are drawn from a comprehensive
taxonomy which harmonizes the modal concepts contributed by the different
studies. We present experiments on the GME corpus aiming to detect and classify
fine-grained modal concepts and associate them with their modified events. We
show that detecting and classifying modal expressions is not only feasible, but
also improves the detection of modal events in their own right.


Abstract:
  Universal difference quotient representations are introduced for the exact
self-sameness principles (SSP) as rules for rates of change introduced in
[Clemence-Mkhope, D.P (2021, Preprint). The Exact Spectral Derivative
Discretization Finite Difference (ESDDFD) Method for Wave Models. arXiv].
Properties are presented for the fundamental rule, a generalized derivative
representation which is shown to yield some known non-integer derivatives as
limit cases of such natural derivative measures; this is shown for some local
derivatives of conformable, fractional, or fractal type and non-local
derivatives of Caputo and Riemann-Liouville type. The SSP-inspired exact
spectral derivative discretization finite difference method is presented for
the Fokker-Planck non-fractional and time-fractional equations; the resulting
discrete models recover exactly some known behaviors predicted for the
processes modeled, such as the Gibbs-Boltzmann distribution and the
Einstein-Stokes-Smoluchowski relation; new ones are predicted.


Abstract:
  Quantum transport simulations often use explicit, yet finite, electronic
reservoirs. These should converge to the correct continuum limit, albeit with a
trade-off between discretization and computational cost. Here, we study this
interplay for extended reservoir simulations, where relaxation maintains a bias
or temperature drop across the system. Our analysis begins in the
non-interacting limit, where we parameterize different discretizations to
compare them on an even footing. For many-body systems, we develop a method to
estimate the relaxation that best approximates the continuum by controlling
virtual transitions in Kramers' turnover for the current. While some
discretizations are more efficient for calculating currents, there is little
benefit with regard to the overall state of the system. Any gains become
marginal for many-body, tensor network simulations, where the relative
performance of discretizations varies when sweeping other numerical controls.
These results indicate that a given reservoir discretization may have little
impact on numerical efficiency for certain computational tools. The choice of a
relaxation parameter, however, is crucial, and the method we develop provides a
reliable estimate of the optimal relaxation for finite reservoirs.


Abstract:
  We report on an all-sky search for continuous gravitational waves in the
frequency band 20-2000\,Hz and with a frequency time derivative in the range of
$[-1.0, +0.1]\times10^{-8}$\,Hz/s. Such a signal could be produced by a nearby,
spinning and slightly non-axisymmetric isolated neutron star in our galaxy.
This search uses the LIGO data from the first six months of Advanced LIGO's and
Advanced Virgo's third observational run, O3. No periodic gravitational wave
signals are observed, and 95\%\ confidence-level (CL) frequentist upper limits
are placed on their strengths. The lowest upper limits on worst-case (linearly
polarized) strain amplitude $h_0$ are $~1.7\times10^{-25}$ near 200\,Hz. For a
circularly polarized source (most favorable orientation), the lowest upper
limits are $\sim6.3\times10^{-26}$. These strict frequentist upper limits refer
to all sky locations and the entire range of frequency derivative values. For a
population-averaged ensemble of sky locations and stellar orientations, the
lowest 95\%\ CL upper limits on the strain amplitude are
$\sim1.\times10^{-25}$. These upper limits improve upon our previously
published all-sky results, with the greatest improvement (factor of $\sim$2)
seen at higher frequencies, in part because quantum squeezing has dramatically
improved the detector noise level relative to the second observational run, O2.
These limits are the most constraining to date over most of the parameter space
searched.


Abstract:
  Cloud-native Applications are 'distributed, elastic and horizontal-scalable
systems composed of (micro)services which isolate states in a minimum of
stateful components'. Hence, an important property is to ensure a low coupling
and a high cohesion among the (micro)services composing the cloud-native
application. Loosely coupled and highly cohesive services allow development
teams to work in parallel, reducing the communication overhead between teams.
However, despite both practitioners and researchers agree on the importance of
this general property, there are no validated metrics to effectively measure or
test the actual coupling level between services. In this work, we propose ways
to compute and visualize the coupling between microservices, by extending and
adapting the concepts behind the computation of the traditional structural
coupling. We validate these measures with a case study involving 17 open-source
projects and we provide an automatic approach to measure them. The results of
this study highlight how these metrics provide to practitioners a quantitative
and visual view of services compositions, which can be useful to conceive
advanced systems to monitor the evolution of the service.


Abstract:
  The low-energy $U(1)_{B-L}$ gauge symmetry is well-motivated as part of
beyond Standard Model physics related to neutrino mass generation. We show that
a light $B-L$ gauge boson $Z{'}$ and the associated $U(1)_{B-L}$-breaking
scalar $\varphi$ can both be effectively searched for at high-intensity
facilities such as the near detector complex of the Deep Underground Neutrino
Experiment (DUNE). Without the scalar $\varphi$, the $Z{'}$ can be probed at
DUNE up to mass of 1 GeV, with the corresponding gauge coupling $g_{BL}$ as low
as $10^{-9}$. In the presence of the scalar $\varphi$ with gauge coupling to
$Z{'}$, the DUNE capability of discovering the gauge boson $Z{'}$ can be
significantly improved, even by one order of magnitude in $g_{BL}$, due to
additional production from the decay $\varphi \to Z{'}Z{'}$. The DUNE
sensitivity is largely complementary to other long-lived $Z{'}$ searches at
beam-dump facilities such as FASER and SHiP, as well as astrophysical and
cosmological probes. On the other hand, the prospects of detecting $\varphi$
itself at DUNE are to some extent weakened in presence of $Z{'}$, compared to
the case without the gauge interaction.


Abstract:
  To calculate the conductivity of a material having full knowledge of its
composition is a reasonably simple task. To do the same in reverse, i.e., to
find information about the composition of a device from its conductivity
response alone, is very challenging and even more so in the presence of
disorder. An inversion methodology capable of decoding the information
contained in the conductivity response of disordered structures has been
recently proposed but despite claims of generality and robustness, the method
has only been used with 2D systems possessing relatively simple electronic
structures. Here we put these claims to the test and generalise the inversion
method to the case of monolayer MoS$_2$, a material whose electronic structure
is far more complex and elaborate. Starting from the spectral function that
describes the DC conductivity of a disordered sample of a single layered
MoS$_2$ containing a small concentration of randomly dispersed vacancies, we
are able to invert the signal and find the exact composition of defects with an
impressive degree of accuracy. Remarkably, equally accurate results are
obtained with the optical conductivity. This is indicative of a methodology
that is indeed suitable to extract composition information from different 2D
materials, regardless of their electronic structure complexity. Calculated
conductivity results were used as a proxy for their experimental counterpart
and were obtained with an efficient quantum transport code (KITE) based on a
real-space multi-orbital tight-binding model with parameters generated by
density functional theory.


Abstract:
  We study the problem of aligning two sets of 3D geometric primitives given
known correspondences. Our first contribution is to show that this primitive
alignment framework unifies five perception problems including point cloud
registration, primitive (mesh) registration, category-level 3D registration,
absolution pose estimation (APE), and category-level APE. Our second
contribution is to propose DynAMical Pose estimation (DAMP), the first general
and practical algorithm to solve primitive alignment problem by simulating
rigid body dynamics arising from virtual springs and damping, where the springs
span the shortest distances between corresponding primitives. We evaluate DAMP
in simulated and real datasets across all five problems, and demonstrate (i)
DAMP always converges to the globally optimal solution in the first three
problems with 3D-3D correspondences; (ii) although DAMP sometimes converges to
suboptimal solutions in the last two problems with 2D-3D correspondences, using
a scheme for escaping local minima, DAMP always succeeds. Our third
contribution is to demystify the surprising empirical performance of DAMP and
formally prove a global convergence result in the case of point cloud
registration by charactering local stability of the equilibrium points of the
underlying dynamical system.


Abstract:
  Recently the Ihara zeta function for the finite graph was extended to
infinite one by Clair and Chinta et al. In this paper, we obtain the same
expressions by a different approach from their analytical method. Our new
approach is to take a suitable limit of a sequence of finite graphs via the
Konno-Sato theorem. This theorem is related to explicit formulas of
characteristic polynomials for the evolution matrix of the Grover walk. The
walk is one of the most well-investigated quantum walks which are quantum
counterpart of classical random walks. We call the relation between the Grover
walk and the zeta function based on the Konno-Sato theorem "Grover/Zeta
Correspondence" here.