Abstract:
  We trace a systematic and consistent method to precisely numerate the
magnitude range for various structural and isospin compositional properties of
the neutron star. Incompressibility, symmetry energy, slope parameter and
curvature of a neutron star are investigated using the relativistic energy
density functional within the framework of coherent density fluctuation model.
The analytical expression for the energy density functional of the neutron star
matter is motivated from the Br$\ddot{u}$ckner functional and acquired by the
polynomial fitting of the saturation curves for three different relativistic
mean-field parameter sets (NL3, G3 and IU-FSU). The modified functional is
folded with the neutron star's density-dependent weight function to calculate
the numerical values for incompressibility and symmetry energy using the
coherent density fluctuation model. NL3 parameter set, being the stiffest
equation of state, endue us with a higher magnitude of all the properties
compared to the other two parameter sets.


Abstract:
  With the improvement of computer performance and the development of
GPU-accelerated technology, trading with machine learning algorithms has
attracted the attention of many researchers and practitioners. In this
research, we propose a novel portfolio management strategy based on the
framework of Deep Deterministic Policy Gradient, a policy-based reinforcement
learning framework, and compare its performance to that of other trading
strategies. In our framework, two Long Short-Term Memory neural networks and
two fully connected neural networks are constructed. We also investigate the
performance of our strategy with and without transaction costs. Experimentally,
we choose eight US stocks consisting of four low-volatility stocks and four
high-volatility stocks. We compare the compound annual return rate of our
strategy against seven other strategies, e.g., Uniform Buy and Hold,
Exponential Gradient and Universal Portfolios. In our case, the compound annual
return rate is 14.12%, outperforming all other strategies. Furthermore, in
terms of Sharpe Ratio (0.5988), our strategy is nearly 33% higher than that of
the second-best performing strategy.


Abstract:
  The recent COVID-19 pandemic has resulted in high fatality rates, especially
for patients who suffer from underlying health issues. One of the more serious
symptoms exhibited from patients suffering from an acute COVID-19 infection is
breathing difficulties and shortness of breath, which is largely due to the
excessive fluid (cellular leakage and cytokine storm) and mucoid debris that
have filled lung alveoli, and reduced the surfactant tension resulting in heavy
and stiff lungs. In this paper, we propose the use of micro-bubbles filled with
exosomes that can be released upon exposure to ultrasound signals as possible
rescue therapy in deteriorating COVID-19 patients. Recent studies have shown
that exosomes can be used to repair and treat lung damage for patients who have
suffered from viral infection. We have conducted simulations to show the
efficacy of the ultrasound signals that will penetrate through layers of
tissues reaching the alveoli that contain the micro-bubbles. Our results have
shown that ultrasound signals with low frequencies are required to oscillate
and rupture the polymer-based micro-bubbles. Our proposed system can be used
for patients who require immediate rescue treatments for lung damage, as well
as for recovered patients who may suffer from viral relapse infection, where
the micro-bubbles will remain dormant for a temporary therapeutic window until
they are exposed to the ultrasound signals.


Abstract:
  In this work, we explore joint energy-based model (EBM) training during the
finetuning of pretrained text encoders (e.g., Roberta) for natural language
understanding (NLU) tasks. Our experiments show that EBM training can help the
model reach a better calibration that is competitive to strong baselines, with
little or no loss in accuracy. We discuss three variants of energy functions
(namely scalar, hidden, and sharp-hidden) that can be defined on top of a text
encoder, and compare them in experiments. Due to the discreteness of text data,
we adopt noise contrastive estimation (NCE) to train the energy-based model. To
make NCE training more effective, we train an auto-regressive noise model with
the masked language model (MLM) objective.


Abstract:
  The Leding-Logarithmic (LL) gluon Sudakov formfactor is derived from
rapidity-ordered BFKL evolution with longitudinal-momentum conservation. This
derivation further clarifies the relation between High-Energy and
TMD-factorizations and can be extended beyond LL-approximation as well.


Abstract:
  The 3D building modelling is important in urban planning and related domains
that draw upon the content of 3D models of urban scenes. Such 3D models can be
used to visualize city images at multiple scales from individual buildings to
entire cities prior to and after a change has occurred. This ability is of
great importance in day-to-day work and special projects undertaken by
planners, geo-designers, and architects. In this research, we implemented a
novel approach to 3D building models for such matter, which included the
integration of geographic information systems (GIS) and 3D Computer Graphics
(3DCG) components that generate 3D house models from building footprints
(polygons), and the automated generation of simple and complex roof geometries
for rapid roof area damage reporting. These polygons (footprints) are usually
orthogonal. A complicated orthogonal polygon can be partitioned into a set of
rectangles. The proposed GIS and 3DCG integrated system partitions orthogonal
building polygons into a set of rectangles and places rectangular roofs and
box-shaped building bodies on these rectangles. Since technicians are drawing
these polygons manually with digitizers, depending on aerial photos, not all
building polygons are precisely orthogonal. But, when placing a set of boxes as
building bodies for creating the buildings, there may be gaps or overlaps
between these boxes if building polygons are not precisely orthogonal. In our
proposal, after approximately orthogonal building polygons are partitioned and
rectified into a set of mutually orthogonal rectangles, each rectangle knows
which rectangle is adjacent to and which edge of the rectangle is adjacent to,
which will avoid unwanted intersection of windows and doors when building
bodies combined.


Abstract:
  In this paper we present an inexact proximal point method for variational
inequality problem on Hadamard manifolds and study its convergence properties.
The proposed algorithm is inexact in two sense. First, each proximal subproblem
is approximated by using the enlargement of the vector field in consideration
and then the next iterated is obtained by solving this subproblem allowing a
suitable error tolerance. As an application, we obtain an inexact proximal
point method for constrained optimization problems, equilibrium problems and
nonlinear optimization problems on Hadamard manifolds.


Abstract:
  The HyperTh{\'e}sau and Bibracte num{\'e}rique projects have given rise to a
collective effort centred on the use of vocabulary as a means of ensuring the
interoperability of archaeological data throughout its life cycle. To this end,
the use of the standardised form of the thesaurus -- via the Opentheso platform
-- provides a tool that is already adapted to the Linked Data. Nevertheless,
its use quickly raised the question of the different paradigms presiding over
the elaboration of a specific vocabulary by each (group of) scientist(s). The
ISO 25964 standard -- designed for the management and interoperability of
indexing languages -- is flexible enough to permit the comparison and linking
of different scientific or documentary ``points of view''. Their coherence
through interoperability alignments nevertheless requires to interface
different semantic granularities: search reporting, the description of raw
data, a gateway or ''pivot'' between the two, by using a regulated cooperation
methodology. The challenges that remain to be met on this path do not prevent
the thesaurus tool from already being a suitable support for a complete
''human-to-machine-to-human'' interoperability, developed within the framework
of the Bibracte Ville Ouverte project and exemplified through a research on the
ceramics of that archaeological site.


Abstract:
  Causal bandit is a nascent learning model where an agent sequentially
experiments in a causal network of variables, in order to identify the
reward-maximizing intervention. Despite the model's wide applicability,
existing analytical results are largely restricted to a parallel bandit version
where all variables are mutually independent. We introduce in this work the
hierarchical causal bandit model as a viable path towards understanding general
causal bandits with dependent variables. The core idea is to incorporate a
contextual variable that captures the interaction among all variables with
direct effects. Using this hierarchical framework, we derive sharp insights on
algorithmic design in causal bandits with dependent arms and obtain nearly
matching regret bounds in the case of a binary context.


Abstract:
  In a Cohen-Macaulay local ring $(A, \mathfrak{m})$, we study the Hilbert
function of an integrally closed $\mathfrak{m}$-primary ideal $I$ whose
reduction number is three. With a mild assumption we give an inequality
$\ell_A(A/I) \ge \mathrm{e}_0(I) - \mathrm{e}_1(I) + \dfrac{\mathrm{e}_2(I) +
\ell_A(I^2/QI)}{2}$, where $\mathrm{e}_i(I)$ denotes the $i$th Hilbert
coefficients and $Q$ denotes a minimal reduction of $I$. The inequality is
located between inequalities of Itoh and Elias-Valla. Furthermore our
inequality becomes an equality if and only if the depth of the associated
graded ring of $I$ is larger than or equal to $\dim A-1$. We also study the
Cohen-Macaulayness of the associated graded rings of determinantal rings.


Abstract:
  Long duration gamma-ray bursts (GRBs) are among the least understood
astrophysical transients powering the high-energy universe. To date, various
mechanisms have been proposed to explain the observed electromagnetic GRB
emission. In this work, we show that, although different jet models may be
equally successful in fitting the observed electromagnetic spectral energy
distributions, the neutrino production strongly depends on the adopted emission
and dissipation model. To this purpose, we compute the neutrino production for
a benchmark high-luminosity GRB in the internal shock model, including a
dissipative photosphere as well as three emission components, in the jet model
invoking internal-collision-induced magnetic reconnection and turbulence
(ICMART), in the case of a magnetic jet with gradual dissipation, and in a jet
with dominant proton synchrotron radiation. We find that the expected neutrino
fluence can vary up to three orders of magnitude in amplitude and peak at
energies ranging from $10^4$ to $10^8$ GeV. For our benchmark input parameters,
none of the explored GRB models is excluded by the targeted searches carried
out by the IceCube and ANTARES Collaborations. However, our work highlights the
potential of high-energy neutrinos of pinpointing the underlying GRB emission
mechanism and the importance of relying on different jet models for unbiased
stacking searches.


Abstract:
  How to learn a good predictor on data with missing values? Most efforts focus
on first imputing as well as possible and second learning on the completed data
to predict the outcome. Yet, this widespread practice has no theoretical
grounding. Here we show that for almost all imputation functions, an
impute-then-regress procedure with a powerful learner is Bayes optimal. This
result holds for all missing-values mechanisms, in contrast with the classic
statistical results that require missing-at-random settings to use imputation
in probabilistic modeling. Moreover, it implies that perfect conditional
imputation is not needed for good prediction asymptotically. In fact, we show
that on perfectly imputed data the best regression function will generally be
discontinuous, which makes it hard to learn. Crafting instead the imputation so
as to leave the regression function unchanged simply shifts the problem to
learning discontinuous imputations. Rather, we suggest that it is easier to
learn imputation and regression jointly. We propose such a procedure, adapting
NeuMiss, a neural network capturing the conditional links across observed and
unobserved variables whatever the missing-value pattern. Experiments confirm
that joint imputation and regression through NeuMiss is better than various two
step procedures in our experiments with finite number of samples.


Abstract:
  Drivers heterogeneity and the broad range of vehicle characteristics are
considered primarily responsible for the stochasticity observed in road traffic
dynamics. Assessing the differences in driving style and incorporating
individual driving behaviour in microsimulation has attracted significant
attention lately. The first topic is studied extensively in the literature. The
second one, on the contrary, remains an open issue. The present study proposes
a methodology to characterise driving style in the free-flow regime and to
incorporate drivers heterogeneity within a microsimulation framework. The
methodology uses explicit and simplified modelling of the vehicle powertrain to
separate the drivers behavior from the vehicle characteristics. Results show
that inter and intra-driver heterogeneity can be captured by log-normal
distributions of well-designed metric.Drivers are classified into three
different groups (dynamic, ordinary and timid drivers).


Abstract:
  We show that any finite affinely independent set can be isometrically
embedded into a regular polygonal torus, that is, a finite product of regular
polygons. As a consequence, with a straightforward application of
K\v{r}\'{i}\v{z}'s theorem, we get an alternative proof of the fact that all
finite affinely independent sets are Ramsey, a result which was originally
proved by Frankl and R\"{o}dl.


Abstract:
  Knowledge of quantum mechanical systems is becoming more important for many
science and engineering students who are looking to join the emerging quantum
workforce. To better prepare a wide range of students for these careers, we
must seek to develop new tools to enhance our education in quantum topics. We
present initial studies on the use of one of these such tools, Quantum
Composer, a 1D quantum simulation and visualization tool developed for
education and research purposes. In particular, we conducted five think-aloud
interviews with students who worked through an exercise using Quantum Composer
that focused on the statics and dynamics of quantum states in single- and
double-harmonic well systems. Our results show that Quantum Composer helps
students to obtain the correct answers to the questions posed, but additional
support is needed to facilitate the development of student reasoning behind
these answers. In addition, we find that students explore familiar and
unfamiliar problems in similar ways, indicating that Quantum Composer is a
useful tool for exploring systems that students have not seen before.


Abstract:
  Regression has attracted immense interest lately due to its effectiveness in
tasks like predicting values. And Regression is of widespread use in multiple
fields such as Economics, Finance, Business, Biology and so on. While
considerable studies have proposed some impressive models, few of them have
provided a whole picture regarding how and to what extent Regression has
developed. With the aim of aiding beginners in understanding the relationships
among different Regression algorithms, this paper characterizes a broad and
thoughtful selection of recent regression algorithms, providing an organized
and comprehensive overview of existing work and models utilized frequently. In
this paper, the relationship between Regression and Deep Learning is also
discussed and a conclusion can be drawn that Deep Learning can be more powerful
as an combination with Regression models in the future.


Abstract:
  Two-dimensional systems with Rashba spin-orbit coupling are not Galilean
invariant and therefore electron--electron collisions in them may affect the
current. However when taken alone, they cannot ensure a nonzero dc resistivity,
so their effects are masked by impurity scattering. Here we calculate the
related finite-frequency response and show that the electron--electron
scattering in clean Rashba conductors decreases the Drude weight while
resulting in a finite dissipative component of the response outside of the
Drude peak.


Abstract:
  Single-crystal (SC) NMR spectroscopy is a solid-state NMR method that has
been used since the early days of NMR to study the magnitude and orientation of
tensorial nuclear spin interactions in solids. This review first presents the
field of SC NMR instrumentation, then provides a survey of software for
analysis of SC NMR data, and finally it highlights selected applications of SC
NMR in various fields of research. The aim of the last part is not to provide a
complete review of all SC NMR literature but to provide examples that
demonstrate interesting applications of SC NMR.


Abstract:
  In semi-supervised graph-based binary classifier learning, a subset of known
labels $\hat{x}_i$ are used to infer unknown labels, assuming that the label
signal $x$ is smooth with respect to a similarity graph specified by a
Laplacian matrix. When restricting labels $x_i$ to binary values, the problem
is NP-hard. While a conventional semi-definite programming (SDP) relaxation can
be solved in polynomial time using, for example, the alternating direction
method of multipliers (ADMM), the complexity of iteratively projecting a
candidate matrix $M$ onto the positive semi-definite (PSD) cone ($M \succeq 0$)
remains high. In this paper, leveraging a recent linear algebraic theory called
Gershgorin disc perfect alignment (GDPA), we propose a fast projection-free
method by solving a sequence of linear programs (LP) instead. Specifically, we
first recast the SDP relaxation to its SDP dual, where a feasible solution $H
\succeq 0$ can be interpreted as a Laplacian matrix corresponding to a balanced
signed graph sans the last node. To achieve graph balance, we split the last
node into two that respectively contain the original positive and negative
edges, resulting in a new Laplacian $\bar{H}$. We repose the SDP dual for
solution $\bar{H}$, then replace the PSD cone constraint $\bar{H} \succeq 0$
with linear constraints derived from GDPA -- sufficient conditions to ensure
$\bar{H}$ is PSD -- so that the optimization becomes an LP per iteration.
Finally, we extract predicted labels from our converged LP solution $\bar{H}$.
Experiments show that our algorithm enjoyed a $40\times$ speedup on average
over the next fastest scheme while retaining comparable label prediction
performance.


Abstract:
  2-local derivation is a generalized derivation for a Lie algebra, which plays
an important role to the study of local properties of the structure of the Lie
algebra. In this paper, we prove that every 2-local derivation on the twisted
Heisenberg-Virasoro algebra is a derivation.


Abstract:
  We use a continuum quark+diquark approach to the nucleon bound-state problem
in relativistic quantum field theory to deliver parameter-free predictions for
the nucleon axial and induced pseudoscalar form factors, $G_A$ and $G_P$, and
unify them with the pseudoscalar form factor $G_5$ or, equivalently, the
pion-nucleon form factor $G_{\pi NN}$. We explain how partial conservation of
the axial-vector current and the associated Goldberger-Treiman relation are
satisfied once all necessary couplings of the external current to the building
blocks of the nucleon are constructed consistently; in particular, we fully
resolve the seagull couplings to the diquark-quark vertices associated with the
axial-vector and pseudoscalar currents. Among the results we describe, the
following are worth highlighting. A dipole form factor defined by an axial
charge $g_A=G_A(0)=1.25(3)$ and a mass-scale $M_A = 1.23(3) m_N$, where $m_N$
is the nucleon mass, can accurately describe the pointwise behavior of $G_A$.
Concerning $G_P$, we obtain the pseudoscalar charge $g_p^\ast = 8.80(23)$, and
find that the pion pole dominance approach delivers a reliable estimate of the
directly computed result. Our computed value of the pion-nucleon coupling
constant, $g_{\pi NN}/m_N =14.02(33)/{\rm GeV}$ is consistent with a
Roy--Steiner-equation analysis of pion-nucleon scattering. We also observe a
marked suppression of the size of the $d$-quark component relative to that of
the $u$-quark in the ratio $g_A^d/g_A^u=-0.16(2)$, which highlights the
presence of strong diquark correlations inside the nucleon -- both scalar and
axial-vector, with the scalar diquark being dominant.


Abstract:
  Modern embedded and cyber-physical systems require every day more
performance, power efficiency and flexibility, to execute several profiles and
functionalities targeting the ever growing adaptivity needs and preserving
execution efficiency. Such requirements pushed designers towards the adoption
of heterogeneous and reconfigurable substrates, which development and
management is not that straightforward. Despite acceleration and flexibility
are desirable in many domains, the barrier of hardware deployment and operation
is still there since specific advanced expertise and skills are needed. Related
challenges are effectively tackled by leveraging on automation strategies that
in some cases, as in the proposed work, exploit model-based approaches. This
paper is focused on the Multi-Dataflow Composer (MDC) tool, that intends to
solve issues related to design, optimization and operation of coarse-grain
reconfigurable hardware accelerators and their easy adoption in modern
heterogeneous substrates. MDC latest features and improvements are introduced
in detail and have been assessed on the so far unexplored robotics application
field. A multi-profile trajectory generator for a robotic arm is implemented
over a Xilinx FPGA board to show in which cases coarse-grain reconfiguration
can be applied and which can be the parameters and trade-offs MDC will allow
users to play with.


Abstract:
  I present results for soft anomalous dimensions through three loops for
several processes involving the production of top quarks. In particular, I
discuss single-top and top-pair production. I also present some numerical
results for double-differential distributions in $t{\bar t}$ production through
approximate N$^3$LO.


Abstract:
  Background. Studies have shown that the conventional left ventricular
mechanical dyssynchrony (LVMD) parameters have their own statistical
limitations. The purpose of this study is to extract new LVMD parameters from
the phase analysis of gated SPECT MPI by deep learning to help CRT patient
selection. Methods. One hundred and three patients who underwent rest gated
SPECT MPI were enrolled in this study. CRT response was defined as a decrease
in left ventricular end-systolic volume (LVESV) >= 15% at 6 +- 1 month follow
up. Autoencoder (AE), an unsupervised deep learning method, was trained by the
raw LV systolic phase polar maps to extract new LVMD parameters, called
AE-based LVMD parameters. Correlation analysis was used to explain the
relationships between new parameters with conventional LVMD parameters.
Univariate and multivariate analyses were used to establish a multivariate
model for predicting CRT response. Results. Complete data were obtained in 102
patients, 44.1% of them were classified as CRT responders. AE-based LVMD
parameter was significant in the univariate (OR 1.24, 95% CI 1.07 - 1.44, P =
0.006) and multivariate analyses (OR 1.03, 95% CI 1.01 - 1.06, P = 0.006).
Moreover, it had incremental value over PSD (AUC 0.72 vs. 0.63, LH 8.06, P =
0.005) and PBW (AUC 0.72 vs. 0.64, LH 7.87, P = 0.005), combined with
significant clinic characteristics, including LVEF and gender. Conclusions. The
new LVMD parameters extracted by autoencoder from the baseline gated SPECT MPI
has the potential to improve the prediction of CRT response.


Abstract:
  Identifying what broken symmetries are present in the cuprates has become a
major area of research. Many authors have reported evidence for so-called "$Q
\sim 0$" order that involves broken inversion, mirror, chiral, or time-reversal
symmetry that is uniform in space. Not all these observations are well
understood and new experimental probes are needed. Here we use resonant soft
x-ray scattering (RSXS) to search for $Q \sim 0$ order in
Bi$_{2.1}$Sr$_{1.9}$CaCu$_2$O$_{8+x}$ (Bi-2212) by measuring the region of a
forbidden Bragg peak, $(0,0,3)$, which is normally extinguished by symmetry but
may become allowed on resonance if valence band order is present. Using
circularly polarized light, we found that this reflection becomes allowed on
the Cu $L_3$ resonance for temperatures $T_c < T < T^\ast$, though remains
absent in linear polarization and at other temperatures. This observation
suggests the existence of spatially uniform valence band order near the
pseudogap temperature. In addition, we observed periodic oscillations in the
specular reflectivity from the sample surface that resemble thin film
interference fringes, though no known film is present. These fringes are highly
resonant, appear in all polarizations, and exhibit a period that depends on the
location where the beam strikes the sample surface. We speculate that these
fringes arise from interaction between some intrinsic valence band instability
and extrinsic structural surface morphologies of the material. Our study
supports the existence of some kind of $Q \sim 0$ broken symmetry state in
Bi-2212 at intermediate temperatures, and calls for further study using a
microfocused beam that could disentangle microscopic effects from macroscopic
heterogeneities.


Abstract:
  We reveal the importance of ongoing in-situ star formation in the Brightest
Cluster Galaxy in the massive cool-core CLASH cluster MACS 1931.8-2635 at
z=0.35. Using a multi-wavelength approach, we assess the stellar and warm
ionized medium components, spatially resolved by the VLT-MUSE spectroscopy, and
link them to the molecular gas by incorporating sub-mm ALMA observations. We
measure the fluxes of strong emission lines, allowing us to determine the
physical conditions of the warm ionized gas. The ionized gas flux brightness
peak corresponds to the location of the supermassive black hole and the system
shows a diffuse warm ionized gas tail extending 30 kpc in N-E direction. The
ionized and molecular gas are co-spatial and co-moving, with the gaseous
component in the tail falling inward, providing fuel for star formation and
accretion-powered nuclear activity. The gas is ionized by a mix of star
formation and other energetic processes which give rise to LINER-like emission,
with active galactic nuclei emission dominant only in the BCG core. We measure
a star formation rate of 97 Msun/yr, with its peak at the BCG core. However,
star formation accounts for only 50-60% of the energetics needed to ionize the
warm gas. In situ star formation generated by thermally unstable intracluster
medium cooling and/or dry mergers dominate the stellar mass growth at z<0.5 and
these mechanisms account for the build-up of 20% of the mass of the system. Our
measurements reveal that the most central regions of the BCG contain the lowest
gas phase oxygen abundance, whereas the tail exhibits slightly more elevated
values. The galaxy is a dispersion dominated system, typical for massive,
elliptical galaxies. The gas and stellar kinematics are decoupled, with the
gaseous velocity fields being more closely related to the bulk motions of the
intracluster medium.


Abstract:
  We uncover a form of quantum contextuality that connects maximal
contextuality to boson indistinguihability in a similar way maximal nonlocality
with respect to the Clauser-Horne-Shimony-Holt Bell inequality is connected to
maximal entanglement. Unlike previous forms of photonic contextuality, this
form cannot be simulated with classical light, as it relies on
indistinguishability and higher-order interference. Ideal measurements on the
bosonic system can be performed by means of dispersive coupling with an
ancillary qubit. This allows us delaying at will the ending of each measurement
and targeting high-dimensional contextual correlations, which are features
which cannot be achieved with existing platforms.


Abstract:
  In this study, we develop a probabilistic approach to map the parametric
uncertainty to the output state uncertainty in first-order hyperbolic
conservation laws. We analyze this problem for nonlinear immiscible two-phase
transport in heterogeneous porous media in the presence of a stochastic
velocity field. The uncertainty in the velocity field can arise from the
incomplete description of either porosity field, injection flux, or both. The
uncertainty in the total-velocity field leads to the spatiotemporal uncertainty
in the saturation field. Given information about the spatial/temporal
statistics of the correlated heterogeneity, we leverage method of distributions
to derive deterministic equations that govern the evolution of single-point CDF
of saturation. Unlike Buckley Leverett equation, the equation for the raw CDF
function is linear in space and time. Hereby, we give routes to circumventing
the computational cost of Monte Carlo scheme while obtaining the full
statistical description of saturation. We conduct a set of numerical
experiments and compare statistics of saturation computed with the method of
distributions, against those obtained using the statistical moment equations
approach and kernel density estimation post-processing of high-resolution Monte
Carlo simulations. This comparison demonstrates that the CDF equations remain
accurate over a wide range of statistical properties, i.e. standard deviation
and correlation length of the underlying random fields, while the corresponding
low-order statistical moment equations significantly deviate from Monte Carlo
results, unless for very small values of standard deviation and correlation
length.


Abstract:
  Data poisoning has been proposed as a compelling defense against facial
recognition models trained on Web-scraped pictures. By perturbing the images
they post online, users can fool models into misclassifying future
(unperturbed) pictures. We demonstrate that this strategy provides a false
sense of security, as it ignores an inherent asymmetry between the parties:
users' pictures are perturbed once and for all before being published (at which
point they are scraped) and must thereafter fool all future models -- including
models trained adaptively against the users' past attacks, or models that use
technologies discovered after the attack. We evaluate two systems for poisoning
attacks against large-scale facial recognition, Fawkes (500,000+ downloads) and
LowKey. We demonstrate how an "oblivious" model trainer can simply wait for
future developments in computer vision to nullify the protection of pictures
collected in the past. We further show that an adversary with black-box access
to the attack can (i) train a robust model that resists the perturbations of
collected pictures and (ii) detect poisoned pictures uploaded online. We
caution that facial recognition poisoning will not admit an "arms race" between
attackers and defenders. Once perturbed pictures are scraped, the attack cannot
be changed so any future successful defense irrevocably undermines users'
privacy.


Abstract:
  The complex unfolding of the US opioid epidemic in the last 20 years has been
the subject of a large body of medical and pharmacological research, and it has
sparked a multidisciplinary discussion on how to implement interventions and
policies to effectively control its impact on public health. This study
leverages Reddit as the primary data source to investigate the opioid crisis.
We aimed to find a large cohort of Reddit users interested in discussing the
use of opioids, trace the temporal evolution of their interest, and extensively
characterize patterns of the nonmedical consumption of opioids, with a focus on
routes of administration and drug tampering. We used a semiautomatic
information retrieval algorithm to identify subreddits discussing nonmedical
opioid consumption, finding over 86,000 Reddit users potentially involved in
firsthand opioid usage. We developed a methodology based on word embedding to
select alternative colloquial and nonmedical terms referring to opioid
substances, routes of administration, and drug-tampering methods. We modeled
the preferences of adoption of substances and routes of administration,
estimating their prevalence and temporal unfolding, observing relevant trends
such as the surge in synthetic opioids like fentanyl and an increasing interest
in rectal administration. Ultimately, through the evaluation of odds ratios
based on co-mentions, we measured the strength of association between opioid
substances, routes of administration, and drug tampering, finding evidence of
understudied abusive behaviors like chewing fentanyl patches and dissolving
buprenorphine sublingually. We believe that our approach may provide a novel
perspective for a more comprehensive understanding of nonmedical abuse of
opioids substances and inform the prevention, treatment, and control of the
public health effects.


Abstract:
  We study the QCD Adler function in the energy region $\approx 0.7-2.5$ GeV,
in which the non-perturbative effects become dominant. Our analysis is a
renormalon-based evaluation using transseries within the resurgence of the
Renormalization-Group-Equation and does not require the
Operator-Product-Expansion.


Abstract:
  We present a proposal to relate the de Sitter Conjecture (dSC) to the
Covariant Entropy Bound (CEB). By assuming an early phase of accelerated
expansion where the CEB is satisfied, we take into account a contribution from
extra-dimensions to the four-dimensional entropy which restricts the values of
the usual slow-roll parameters. We show in this context that the dSC
inequalities follow from the CEB -- including their mutual exclusion -- in both
single and multi-field inflationary scenarios. We also observe that the order
one constants, c and c' in the conjecture are given in terms of physical
quantities such as the change in entropy over time, the Hubble constant and the
dynamics of the effective scalar fields. Finally, we give a simple example to
illustrate a possible contribution to the four-dimensional entropy from a flux
string scenario.


Abstract:
  The Hall effect arises when time reversal symmetry is broken by either
intrinsic magnetism or an external magnetic field. The latter contribution
dominates in non-magnetic materials, in which the angular dependence of the
Hall effect is typically a smooth cosine function because only the out-of-plane
projection of the field generates the in-plane transverse motion of electrons.
Here, we report the observation of an abrupt switching of the Hall effect by
field rotation in a non-magnetic material, ZrTe5. The angular dependence of the
Hall resistivity approaches a signum function, persisting down to an extremely
low field of 0.03 T. By varying the carrier density of ZrTe5 over three orders
of magnitude, we show that this singular behavior is due to the anomalous Hall
effect generated by the ultra-dilute massive Dirac carriers in the quantum
limit of Pauli paramagnetism when the Zeeman energy exceeds the Fermi energy.
Our results elucidate the origin of the anomalous Hall effect in ZrTe5, arising
owing to the spin-polarized massive Dirac electrons rather than the separation
of Weyl nodes.


Abstract:
  The objective of the present paper is to study 4-dimensional almost pseudo
Ricci symmetric perfect fluid spacetimes $(APRS)_4$. We show that a
Robertson-Walker spacetime is $(APRS)_4$ and vice versa under certain condition
imposed on its scale factor. Some popular toy models of $F(R)$-gravity are also
studied under the current setting and various energy conditions are
investigated.


Abstract:
  The recently discovered family of AV$_3$Sb$_5$ (A: K, Rb Cs) kagome metals
possess a unique combination of nontrivial band topology, superconducting
ground states, and signatures of electron correlations manifest via competing
charge density wave order. Little is understood regarding the nature of the
charge density wave (CDW) instability inherent to these compounds and the
potential correlation with the accompanying onset of a large anomalous Hall
response. To understand the impact of the CDW order on the electronic structure
in these systems, we present quantum oscillation measurements on single
crystals of CsV$_3$Sb$_5$. Our data provides direct evidence that the CDW
invokes a substantial reconstruction of the Fermi surface pockets associated
with the vanadium orbitals and the kagome lattice framework. In conjunction
with density functional theory modeling, we are able to identify split
oscillation frequencies originating from reconstructed pockets built from
vanadium orbitals and Dirac-like bands. Complementary diffraction measurements
are further able to demonstrate that the CDW instability has a correlated
phasing between neighboring V$_3$Sb$_5$ planes. These results provide critical
insights into the underlying CDW instability in AV$_3$Sb$_5$ kagome metals and
support minimal models of CDW order arising from within the vanadium-based
kagome lattice.


Abstract:
  Detectors based upon the noble elements, especially liquid xenon as well as
liquid argon, as both single- and dual-phase types, require reconstruction of
the energies of interacting particles, both in the field of direct detection of
dark matter (Weakly Interacting Massive Particles or WIMPs, axions, etc.) and
in neutrino physics. Experimentalists, as well as theorists who
reanalyze/reinterpret experimental data, have used a few different techniques
over the past few decades. In this paper, we review techniques based on solely
the primary scintillation channel, the ionization or secondary channel
available at non-zero drift electric fields, and combined techniques that
include a simple linear combination and weighted averages, with a brief
discussion of the applications of profile likelihood, maximum likelihood, and
machine learning. Comparing results for electron recoils (beta and gamma
interactions) and nuclear recoils (primarily from neutrons) from the Noble
Element Simulation Technique (NEST) simulation to available data, we confirm
that combining all available information generates higher-precision means,
lower widths (energy resolution), and more symmetric shapes (approximately
Gaussian) especially at keV-scale energies, with the symmetry even greater when
thresholding is addressed. Near thresholds, bias from upward fluctuations
matters. For MeV-GeV scales, if only one channel is utilized, an
ionization-only-based energy scale outperforms scintillation; channel
combination remains beneficial. We discuss here what major collaborations use.


Abstract:
  We introduce Habitat 2.0 (H2.0), a simulation platform for training virtual
robots in interactive 3D environments and complex physics-enabled scenarios. We
make comprehensive contributions to all levels of the embodied AI stack - data,
simulation, and benchmark tasks. Specifically, we present: (i) ReplicaCAD: an
artist-authored, annotated, reconfigurable 3D dataset of apartments (matching
real spaces) with articulated objects (e.g. cabinets and drawers that can
open/close); (ii) H2.0: a high-performance physics-enabled 3D simulator with
speeds exceeding 25,000 simulation steps per second (850x real-time) on an
8-GPU node, representing 100x speed-ups over prior work; and, (iii) Home
Assistant Benchmark (HAB): a suite of common tasks for assistive robots (tidy
the house, prepare groceries, set the table) that test a range of mobile
manipulation capabilities. These large-scale engineering contributions allow us
to systematically compare deep reinforcement learning (RL) at scale and
classical sense-plan-act (SPA) pipelines in long-horizon structured tasks, with
an emphasis on generalization to new objects, receptacles, and layouts. We find
that (1) flat RL policies struggle on HAB compared to hierarchical ones; (2) a
hierarchy with independent skills suffers from 'hand-off problems', and (3) SPA
pipelines are more brittle than RL policies.


Abstract:
  The Tunka Radio Extension (Tunka-Rex) is a digital antenna array (63 antennas
distributed over 1km^2) co-located with the TAIGA observatory in Eastern
Siberia. Tunka-Rex measures radio emission of air-showers induced by ultra-high
energy cosmic rays in the frequency band of 30-80 MHz. Air-shower signal is a
short (tens of nanoseconds) broadband pulse. Using time positions and
amplitudes of these pulses, we reconstruct parameters of air showers and
primary cosmic rays. The amplitudes of low-energy event (E<10^17 eV) cannot be
used for successful reconstruction due to the domination of background. To
lower the energy threshold of the detection and increase the efficiency, we use
autoencoder neural network which removes noise from the measured data. This
work describes our approach to denoising raw data and further reconstruction of
air-shower parameters. We also present results of the low-energy events
reconstruction with autoencoder.


Abstract:
  We prove that iterating projections onto convex subsets of Hadamard spaces
can behave in a more complicated way than in Hilbert spaces, resolving a
problem formulated by Miroslav Ba\v{c}\'ak.


Abstract:
  We study vote delegation with "well-behaving" and "misbehaving" agents and
compare it with conventional voting. Typical examples for vote delegation are
validation or governance tasks on blockchains. There is a majority of
well-behaving agents, but they may abstain or delegate their vote to other
agents since voting is costly. Misbehaving agents always vote. We compare
conventional voting allowing for abstention with vote delegation. Preferences
of voters are private information and a positive outcome is achieved if
well-behaving agents win. We illustrate that vote delegation leads to quite
different outcomes than conventional voting with abstention. In particular, we
obtain three insights: First, if the number of misbehaving voters, denoted by f
, is high, both voting methods fail to deliver a positive outcome. Second, if f
takes an intermediate value, conventional voting delivers a positive outcome,
while vote delegation fails with probability one. Third, if f is low,
delegation delivers a positive outcome with higher probability than
conventional voting. Finally, our results characterize worst-case outcomes that
can happen in a liquid democracy.


Abstract:
  In this paper, we extend a framework that we developed earlier for
coordination of connected and automated vehicles (CAVs) at a signal-free
intersection to incorporate uncertainty. Using the possibly noisy observations
of actual time trajectories and leveraging Gaussian process regression, we
learn the bounded confidence intervals for deviations from the nominal
trajectories of CAVs online. Incorporating these confidence intervals, we
reformulate the trajectory planning as a robust coordination problem, the
solution of which guarantees that constraints in the system are satisfied in
the presence of bounded deviations from the nominal trajectories. We
demonstrate the effectiveness of our extended framework through a numerical
simulation.


Abstract:
  A typical part of learning to play the piano is the progression through a
series of practice units that focus on individual dimensions of the skill, such
as hand coordination, correct posture, or correct timing. Ideally, a focus on a
particular practice method should be made in a way to maximize the learner's
progress in learning to play the piano. Because we each learn differently, and
because there are many choices for possible piano practice tasks and methods,
the set of practice tasks should be dynamically adapted to the human learner.
However, having a human teacher guide individual practice is not always
feasible since it is time consuming, expensive, and not always available.
Instead, we suggest to optimize in the space of practice methods, the so-called
practice modes. The proposed optimization process takes into account the skills
of the individual learner and their history of learning. In this work we
present a modeling framework to guide the human learner through the learning
process by choosing practice modes that have the highest expected utility
(i.e., improvement in piano playing skill). To this end, we propose a human
learner utility model based on a Gaussian process, and exemplify the model
training and its application for practice scaffolding on an example of
simulated human learners.


Abstract:
  Model-informed precision dosing (MIPD) is a quantitative dosing framework
that combines prior knowledge on the drug-disease-patient system with patient
data from therapeutic drug/ biomarker monitoring (TDM) to support
individualized dosing in ongoing treatment.Structural models and prior
parameter distributions used in MIPD approaches typically build on prior
clinical trials that involve only a limited number of patients selected
according to some exclusion/inclusion criteria. Compared to the prior clinical
trial population, the patient population in clinical practice can be expected
to include also altered behavior and/or increased interindividual variability,
the extent of which, however, is typically unknown. Here, we address the
question of how to adapt and refine models on the level of the model parameters
to better reflect this real-world diversity. We propose an approach for
continued learning across patients during MIPD using a sequential hierarchical
Bayesian framework. The approach builds on two stages to separate the update of
the individual patient parameters from updating the population parameters.
Consequently, it enables continued learning across hospitals or study centers,
since only summary patient data (on the level of model parameters) need to be
shared, but no individual TDM data. We illustrate this continued learning
approach with neutrophil-guided dosing of paclitaxel. The present study
constitutes an important step towards building confidence in MIPD and
eventually establishing MIPD increasingly in everyday therapeutic use.


Abstract:
  In recent years, chatbots have been empowered to engage in social
conversations with humans and have the potential to elicit people to disclose
their personal experiences, opinions, and emotions. However, how and to what
extent people respond to chabots' self-disclosure remain less known. In this
work, we designed a social chatbot with three self-disclosure levels that
conducted small talks and provided relevant recommendations to people. 372
MTurk participants were randomized to one of the four groups with different
self-disclosure levels to converse with the chatbot on two topics, movies, and
COVID-19. We found that people's self-disclosure level was strongly reciprocal
to a chatbot's self-disclosure level. Chatbots' self-disclosure also positively
impacted engagement and users' perception of the bot and led to a more
effective recommendation such that participants enjoyed and agreed more with
the recommendations.


Abstract:
  In this paper, we propose an RNN-Transducer model for recognizing Japanese
and Chinese offline handwritten text line images. As far as we know, it is the
first approach that adopts the RNN-Transducer model for offline handwritten
text recognition. The proposed model consists of three main components: a
visual feature encoder that extracts visual features from an input image by CNN
and then encodes the visual features by BLSTM; a linguistic context encoder
that extracts and encodes linguistic features from the input image by embedded
layers and LSTM; and a joint decoder that combines and then decodes the visual
features and the linguistic features into the final label sequence by fully
connected and softmax layers. The proposed model takes advantage of both visual
and linguistic information from the input image. In the experiments, we
evaluated the performance of the proposed model on the two datasets: Kuzushiji
and SCUT-EPT. Experimental results show that the proposed model achieves
state-of-the-art performance on all datasets.


Abstract:
  Large-scale quantum devices provide insights beyond the reach of classical
simulations. However, for a reliable and verifiable quantum simulation, the
building blocks of the quantum device require exquisite benchmarking. This
benchmarking of large scale dynamical quantum systems represents a major
challenge due to lack of efficient tools for their simulation. Here, we present
a scalable algorithm based on neural networks for Hamiltonian tomography in
out-of-equilibrium quantum systems. We illustrate our approach using a model
for a forefront quantum simulation platform: ultracold atoms in optical
lattices. Specifically, we show that our algorithm is able to reconstruct the
Hamiltonian of an arbitrary size quasi-1D bosonic system using an accessible
amount of experimental measurements. We are able to significantly increase the
previously known parameter precision.


Abstract:
  In this work we investigate the Wigner localization of two interacting
electrons at very low density in two and three dimensions using the exact
diagonalization of the many-body Hamiltonian. We use our recently developed
method based on Clifford periodic boundary conditions with a renormalized
distance in the Coulomb potential. To accurately represent the electronic wave
function we use a regular distribution in space of gaussian-type orbitals and
we take advantage of the translational symmetry of the system to efficiently
calculate the electronic wave function. We are thus able to accurately describe
the wave function up to very low density. We validate our approach by comparing
our results to a semi-classical model that becomes exact in the low-density
limit. With our approach we are able to observe the Wigner localization without
ambiguity.


Abstract:
  Several novel imaging and non-destructive testing technologies are based on
reconstructing the spatially dependent coefficient in an elliptic partial
differential equation from measurements of its solution(s). In practical
applications, the unknown coefficient is often assumed to be piecewise constant
on a given pixel partition (corresponding to the desired resolution), and only
finitely many measurements can be made. This leads to the problem of inverting
a finite-dimensional non-linear forward operator $\mathcal F:\ \mathcal
D(\mathcal F)\subseteq \mathbb R^n\to \mathbb R^m$, where evaluating $\mathcal
F$ requires one or several PDE solutions.
  Numerical inversion methods require the implementation of this forward
operator and its Jacobian. We show how to efficiently implement both using a
standard FEM package and prove convergence of the FEM approximations against
their true-solution counterparts. We present simple example codes for Comsol
with the Matlab Livelink package, and numerically demonstrate the challenges
that arise from non-uniqueness, non-linearity and instability issues. We also
discuss monotonicity and convexity properties of the forward operator that
arise for symmetric measurement settings.


Abstract:
  Substantial improvements have been made in machine reading comprehension,
where the machine answers questions based on a given context. Current
state-of-the-art models even surpass human performance on several benchmarks.
However, their abilities in the cross-lingual scenario are still to be
explored. Previous works have revealed the abilities of pre-trained
multilingual models for zero-shot cross-lingual reading comprehension. In this
paper, we further utilized unlabeled data to improve the performance. The model
is first supervised-trained on source language corpus, and then self-trained
with unlabeled target language data. The experiment results showed improvements
for all languages, and we also analyzed how self-training benefits
cross-lingual reading comprehension in qualitative aspects.


Abstract:
  In order to improve the energy efficiency in district heating networks and
the comfort of their customers, these networks need to overcome the problem of
unfair heat distribution under heat deficits. This paper introduces a new
strategy to achieve this thermal fairness objective: it is low-cost in terms of
communication and computation. The proposed approach is illustrated on a
simulation example.


Abstract:
  This paper presents an emotion-regularized conditional variational
autoencoder (Emo-CVAE) model for generating emotional conversation responses.
In conventional CVAE-based emotional response generation, emotion labels are
simply used as additional conditions in prior, posterior and decoder networks.
Considering that emotion styles are naturally entangled with semantic contents
in the language space, the Emo-CVAE model utilizes emotion labels to regularize
the CVAE latent space by introducing an extra emotion prediction network. In
the training stage, the estimated latent variables are required to predict the
emotion labels and token sequences of the input responses simultaneously.
Experimental results show that our Emo-CVAE model can learn a more informative
and structured latent space than a conventional CVAE model and output responses
with better content and emotion performance than baseline CVAE and
sequence-to-sequence (Seq2Seq) models.


Abstract:
  In this correspondence, we study the physical layer security in a stochastic
unmanned aerial vehicles (UAVs) network from a network-wide perspective, where
the locations of UAVs are modeled as a Mat$\acute{\text{e}}$rn hard-core point
process (MHCPP) to characterize the minimum safety distance between UAVs, and
the locations of users and eavesdroppers are modeled as a Poisson cluster
process and a Poisson point process, respectively. UAVs adopt zero-forcing
precoding to serve multiple ground users and emit artificial noise to combat
eavesdropping. We derive the approximations for the coverage probability and
secrecy probability of a typical user, with which we derive the secrecy
throughput of the whole network. Numerical results show the analytical results
can well approximate the simulation results. Impacts of parameters on the
secrecy performance are shown.


Abstract:
  Distributed implementations are crucial in speeding up large scale machine
learning applications. Distributed gradient descent (GD) is widely employed to
parallelize the learning task by distributing the dataset across multiple
workers. A significant performance bottleneck for the per-iteration completion
time in distributed synchronous GD is $straggling$ workers. Coded distributed
computation techniques have been introduced recently to mitigate stragglers and
to speed up GD iterations by assigning redundant computations to workers. In
this paper, we consider gradient coding (GC), and propose a novel dynamic GC
scheme, which assigns redundant data to workers to acquire the flexibility to
dynamically choose from among a set of possible codes depending on the past
straggling behavior. In particular, we consider GC with clustering, and
regulate the number of stragglers in each cluster by dynamically forming the
clusters at each iteration; hence, the proposed scheme is called $GC$ $with$
$dynamic$ $clustering$ (GC-DC). Under a time-correlated straggling behavior,
GC-DC gains from adapting to the straggling behavior over time such that, at
each iteration, GC-DC aims at distributing the stragglers across clusters as
uniformly as possible based on the past straggler behavior. For both
homogeneous and heterogeneous worker models, we numerically show that GC-DC
provides significant improvements in the average per-iteration completion time
without an increase in the communication load compared to the original GC
scheme.


Abstract:
  The choice of negative examples is important in noise contrastive estimation.
Recent works find that hard negatives -- highest-scoring incorrect examples
under the model -- are effective in practice, but they are used without a
formal justification. We develop analytical tools to understand the role of
hard negatives. Specifically, we view the contrastive loss as a biased
estimator of the gradient of the cross-entropy loss, and show both
theoretically and empirically that setting the negative distribution to be the
model distribution results in bias reduction. We also derive a general form of
the score function that unifies various architectures used in text retrieval.
By combining hard negatives with appropriate score functions, we obtain strong
results on the challenging task of zero-shot entity linking.


Abstract:
  We demonstrate the design of a matterwave interferometer to measure
acceleration in one dimension with high precision. The system we base this on
consists of ultracold atoms in an optical lattice potential created by
interfering laser beams. Our approach uses reinforcement learning, a branch of
machine learning, that generates the protocols needed to realize lattice-based
analogs of optical components including a beam splitter, a mirror, and a
recombiner. The performance of these components is evaluated by comparison with
their optical analogs. The interferometer's sensitivity to acceleration is
quantitatively evaluated using a Bayesian statistical approach. We find the
sensitivity to surpass that of standard Bragg interferometry, demonstrating the
future potential for this design methodology.


Abstract:
  Simultaneous translation, which starts translating each sentence after
receiving only a few words in source sentence, has a vital role in many
scenarios. Although the previous prefix-to-prefix framework is considered
suitable for simultaneous translation and achieves good performance, it still
has two inevitable drawbacks: the high computational resource costs caused by
the need to train a separate model for each latency $k$ and the insufficient
ability to encode information because each target token can only attend to a
specific source prefix. We propose a novel framework that adopts a simple but
effective decoding strategy which is designed for full-sentence models. Within
this framework, training a single full-sentence model can achieve arbitrary
given latency and save computational resources. Besides, with the competence of
the full-sentence model to encode the whole sentence, our decoding strategy can
enhance the information maintained in the decoded states in real time.
Experimental results show that our method achieves better translation quality
than baselines on 4 directions: Zh$\rightarrow$En, En$\rightarrow$Ro and
En$\leftrightarrow$De.


Abstract:
  Explainable machine learning (ML) has gained traction in recent years due to
the increasing adoption of ML-based systems in many sectors. Algorithmic
Recourses (ARs) provide "what if" feedback of the form "if an input datapoint
were x' instead of x, then an ML-based system's output would be y' instead of
y." ARs are attractive due to their actionable feedback, amenability to
existing legal frameworks, and fidelity to the underlying ML model. Yet,
current AR approaches are single shot -- that is, they assume x can change to
x' in a single time period. We propose a novel stochastic-control-based
approach that generates sequential ARs, that is, ARs that allow x to move
stochastically and sequentially across intermediate states to a final state x'.
Our approach is model agnostic and black box. Furthermore, the calculation of
ARs is amortized such that once trained, it applies to multiple datapoints
without the need for re-optimization. In addition to these primary
characteristics, our approach admits optional desiderata such as adherence to
the data manifold, respect for causal relations, and sparsity -- identified by
past research as desirable properties of ARs. We evaluate our approach using
three real-world datasets and show successful generation of sequential ARs that
respect other recourse desiderata.


Abstract:
  We study the effects of including a nonzero positron-to-electron fraction in
emitting plasma on the polarized SEDs and sub-millimeter images of jet and
accretion flow models for near-horizon emission from M87* and Sgr A*. For M87*,
we consider a semi-analytic fit to the force-free plasma regions of a general
relativistic magnetohydrodynamic jet simulation which we populate with
power-law leptons with a constant electron-to-magnetic pressure ratio. For Sgr
A*, we consider a standard self-similar radiatively inefficient accretion flow
where the emission is predominantly from thermal leptons with a small fraction
in a power-law tail. In both models, we fix the positron-to-electron ratio
throughout the emission region. We generate polarized images and spectra from
our models using the general-relativistic ray tracing and radiative transfer
from GRTRANS. We find that a substantial positron fraction reduces the circular
polarization fraction at infrared and higher frequencies. However, in
sub-millimeter images higher positron fractions increase polarization fractions
due to strong effects of Faraday conversion. We find a M87* jet model that best
matches the available broadband total intensity and 230 GHz polarization data
is a sub-equipartition, with positron fraction of $\simeq$ 10%. We show that
jet models with significant positron fractions do not satisfy the polarimetric
constraints at 230 GHz from the Event Horizon Telescope (EHT). Sgr A* models
show similar trends in their polarization fractions with increasing pair
fraction. Both models suggest that resolved, polarized EHT images are useful to
constrain the presence of pairs at 230 GHz emitting regions of M87* and Sgr A*.


Abstract:
  Humans and other intelligent animals evolved highly sophisticated perception
systems that combine multiple sensory modalities. On the other hand,
state-of-the-art artificial agents rely mostly on visual inputs or structured
low-dimensional observations provided by instrumented environments. Learning to
act based on combined visual and auditory inputs is still a new topic of
research that has not been explored beyond simple scenarios. To facilitate
progress in this area we introduce a new version of VizDoom simulator to create
a highly efficient learning environment that provides raw audio observations.
We study the performance of different model architectures in a series of tasks
that require the agent to recognize sounds and execute instructions given in
natural language. Finally, we train our agent to play the full game of Doom and
find that it can consistently defeat a traditional vision-based adversary. We
are currently in the process of merging the augmented simulator with the main
ViZDoom code repository. Video demonstrations and experiment code can be found
at https://sites.google.com/view/sound-rl.


Abstract:
  In no-reference 360-degree image quality assessment (NR 360IQA), graph
convolutional networks (GCNs), which model interactions between viewports
through graphs, have achieved impressive performance. However, prevailing
GCN-based NR 360IQA methods suffer from three main limitations. First, they
only use high-level features of the distorted image to regress the quality
score, while the human visual system (HVS) scores the image based on
hierarchical features. Second, they simplify complex high-order interactions
between viewports in a pairwise fashion through graphs. Third, in the graph
construction, they only consider spatial locations of viewports, ignoring its
content characteristics. Accordingly, to address these issues, we propose an
adaptive hypergraph convolutional network for NR 360IQA, denoted as AHGCN.
Specifically, we first design a multi-level viewport descriptor for extracting
hierarchical representations from viewports. Then, we model interactions
between viewports through hypergraphs, where each hyperedge connects two or
more viewports. In the hypergraph construction, we build a location-based
hyperedge and a content-based hyperedge for each viewport. Experimental results
on two public 360IQA databases demonstrate that our proposed approach has a
clear advantage over state-of-the-art full-reference and no-reference IQA
models.


Abstract:
  We have grown single crystals of Na$_2$BaNi(PO$_4$)$_2$, a new spin-1
equilateral triangular lattice antiferromagnet (ETLAF), and performed magnetic
susceptibility, specific heat and thermal conductivity measurements at ultralow
temperatures. The main results are (i) at zero magnetic field,
Na$_2$BaNi(PO$_4$)$_2$ exhibits a magnetic ordering at 430 mK with a weak
ferromagnetic moment along the $c$ axis. This suggests a canted 120$^\circ$
spin structure, which is in a plane including the crystallographic $c$ axis due
to the existence of an easy-axis anisotropy and ferromagnetically stacked along
the $c$ axis; (ii) with increasing field along the $c$ axis, a 1/3
magnetization plateau is observed which means the canted 120$^\circ$ spin
structure is transformed to a up up down (UUD) spin structure. With even higher
fields, the UUD phase further evolves to possible V and V' phases; (iii) with
increasing field along the $a$ axis, the canted 120$^\circ$ spin structure is
possibly transformed to a umbrella phase and a V phase. Therefore,
Na$_2$BaNi(PO$_4$)$_2$ is a rare example of spin-1 ETLAF with single
crystalline form to exhibit easy-axis spin anisotropy and series of quantum
spin state transitions.


Abstract:
  As the curation of data for machine learning becomes increasingly automated,
dataset tampering is a mounting threat. Backdoor attackers tamper with training
data to embed a vulnerability in models that are trained on that data. This
vulnerability is then activated at inference time by placing a "trigger" into
the model's input. Typical backdoor attacks insert the trigger directly into
the training data, although the presence of such an attack may be visible upon
inspection. In contrast, the Hidden Trigger Backdoor Attack achieves poisoning
without placing a trigger into the training data at all. However, this hidden
trigger attack is ineffective at poisoning neural networks trained from
scratch. We develop a new hidden trigger attack, Sleeper Agent, which employs
gradient matching, data selection, and target model re-training during the
crafting process. Sleeper Agent is the first hidden trigger backdoor attack to
be effective against neural networks trained from scratch. We demonstrate its
effectiveness on ImageNet and in black-box settings. Our implementation code
can be found at https://github.com/hsouri/Sleeper-Agent.


Abstract:
  In this letter, we investigate the hybrid beamforming based on deep
reinforcement learning (DRL) for millimeter Wave (mmWave) multi-user (MU)
multiple-input-single-output (MISO) system. A multi-agent DRL method is
proposed to solve the exploration efficiency problem in DRL. In the proposed
method, prioritized replay buffer and more informative reward are applied to
accelerate the convergence. Simulation results show that the proposed
architecture achieves higher spectral efficiency and less time consumption than
the benchmarks, thus is more suitable for practical applications.


Abstract:
  The geometric structure of S-matrix encapsulated by the "Amplituhedron
program" has begun to reveal itself even in non-supersymmetric quantum field
theories. Starting with the seminal work of Arkani-Hamed, Bai, He and Yan it is
now understood that for a wide class of scalar quantum field theories,
tree-level amplitudes are canonical forms associated to polytopes known as
accordiohedra. Similarly the higher loop scalar integrands are canonical forms
associated to so called type-D cluster polytopes for cubic interactions or
recently discovered class of polytopes termed pseudo-accordiohedron for higher
order scalar interactions.
  In this paper, we continue to probe the universality of these structures for
a wider class of scalar quantum field theories. More in detail, we discover new
realisations of the associahedron in planar kinematic space whose canonical
forms generate (colour-ordered) tree-level S matrix of external massless
particles with $n-4$ massless poles and one massive pole at $m^{2}$. The
resulting amplitudes are associated to $\lambda_{1}\, \phi_{1}^{3}\, +\,
\lambda_{2}\, \phi_{1}^{2}\phi_{2}$ potential where $\phi_{1}$ and $\phi_{2}$
are massless and massive scalar fields with bi-adjoint colour indices
respectively. We also show how in the "decoupling limit" (where $m \rightarrow
\infty, \lambda_{2} \rightarrow \infty$ such that $g := \frac{\lambda_{2}}{m} =
\textrm{finite}$) these associahedra project onto a specific class of
accordiohedron which are known to be positive geometries of amplitudes
generated by $\lambda \phi_{1}^{3} + g \phi_{1}^{4}$.


Abstract:
  Much recent research has been dedicated to improving the efficiency of
training and inference for image classification. This effort has commonly
focused on explicitly improving theoretical efficiency, often measured as
ImageNet validation accuracy per FLOP. These theoretical savings have, however,
proven challenging to achieve in practice, particularly on high-performance
training accelerators.
  In this work, we focus on improving the practical efficiency of the
state-of-the-art EfficientNet models on a new class of accelerator, the
Graphcore IPU. We do this by extending this family of models in the following
ways: (i) generalising depthwise convolutions to group convolutions; (ii)
adding proxy-normalized activations to match batch normalization performance
with batch-independent statistics; (iii) reducing compute by lowering the
training resolution and inexpensively fine-tuning at higher resolution. We find
that these three methods improve the practical efficiency for both training and
inference. Code available at
https://github.com/graphcore/graphcore-research/tree/main/Making_EfficientNet_More_Efficient .


Abstract:
  We describe a quantum mechanical measurement as a variational principle
including interaction between the system under measurement and the measurement
apparatus. Augmenting the action with a nonlocal term (a double integration
over the duration of the interaction) results in a theory capable of describing
both the measurement process (agreement between system state and pointer state)
and the collapse of both systems into a single eigenstate (or superposition of
degenerate eigenstates) of the relevant operator. In the absence of the
interaction, a superposition of states is stable, and the theory agrees with
the predictions of standard quantum theory. Because the theory is nonlocal, the
resulting wave equation is an integrodifferential equation (IDE). We
demonstrate these ideas using a simple Lagrangian for both systems, as proof of
principle. The variational principle is time-symmetric and retrocausal, so the
solution for the measurement process is determined by boundary conditions at
both initial and final times; the initial condition is determined by the
experimental preparation and the final condition is the natural boundary
condition of variational calculus. We hypothesize that one or more hidden
variables (not ruled out by Bell's Theorem, due both to the retrocausality and
the nonlocality of the theory) influence the outcome of the measurement, and
that distributions of the hidden variables that arise plausibly in a typical
ensemble of experimental realizations give rise to outcome frequencies
consistent with Born's rule. We outline steps in a theoretical validation of
the hypothesis. We discuss the role of both initial and final conditions to
determine a solution at intermediate times, the mechanism by which a system
responds to measurement, time symmetry of the new theory, causality concerns,
and issues surrounding solution of the IDE.


Abstract:
  We introduce a new class $\mathcal{G}$ of bipartite plane graphs and prove
that each graph in $\mathcal{G}$ admits a proper square contact representation.
A contact between two squares is \emph{proper} if they intersect in a line
segment of positive length. The class $\mathcal{G}$ is the family of
quadrangulations obtained from the 4-cycle $C_4$ by successively inserting a
single vertex or a 4-cycle of vertices into a face.
  For every graph $G\in \mathcal{G}$, we construct a proper square contact
representation. The key parameter of the recursive construction is the aspect
ratio of the rectangle bounded by the four outer squares. We show that this
aspect ratio may continuously vary in an interval $I_G$. The interval $I_G$
cannot be replaced by a fixed aspect ratio, however, as we show, the feasible
interval $I_G$ may be an arbitrarily small neighborhood of any positive real.


Abstract:
  In this paper, we present a new ellipsoid-type algorithm for solving
nonsmooth problems with convex structure. Examples of such problems include
nonsmooth convex minimization problems, convex-concave saddle-point problems
and variational inequalities with monotone operator. Our algorithm can be seen
as a combination of the standard Subgradient and Ellipsoid methods. However, in
contrast to the latter one, the proposed method has a reasonable convergence
rate even when the dimensionality of the problem is sufficiently large. For
generating accuracy certificates in our algorithm, we propose an efficient
technique, which ameliorates the previously known recipes.


Abstract:
  We consider an energy system with $n$ consumers who are linked by a Demand
Side Management (DSM) contract, i.e. they agreed to diminish, at random times,
their aggregated power consumption by a predefined volume during a predefined
duration. Their failure to deliver the service is penalised via the difference
between the sum of the $n$ power consumptions and the contracted target. We are
led to analyse a non-zero sum stochastic game with $n$ players, where the
interaction takes place through a cost which involves a delay induced by the
duration included in the DSM contract. When $n \to \infty$, we obtain a
Mean-Field Game (MFG) with random jump time penalty and interaction on the
control. We prove a stochastic maximum principle in this context, which allows
to compare the MFG solution to the optimal strategy of a central planner. In a
linear quadratic setting we obtain an semi-explicit solution through a system
of decoupled forward-backward stochastic differential equations with jumps,
involving a Riccati Backward SDE with jumps. We show that it provides an
approximate Nash equilibrium for the original $n$-player game for $n$ large.
Finally, we propose a numerical algorithm to compute the MFG equilibrium and
present several numerical experiments.


Abstract:
  Reproducing Kernel Hilbert Space (RKHS) is the common mathematical platform
for various kernel methods in machine learning. The purpose of kernel learning
is to learn an appropriate RKHS according to different machine learning
scenarios and training samples. Because RKHS is uniquely generated by the
kernel function, kernel learning can be regarded as kernel function learning.
This paper proposes a Domain Adaptive Learning method based on Sample-Dependent
and Learnable Kernels (SDLK-DAL). The first contribution of our work is to
propose a sample-dependent and learnable Positive Definite Quadratic Kernel
function (PDQK) framework. Unlike learning the exponential parameter of
Gaussian kernel function or the coefficient of kernel combinations, the
proposed PDQK is a positive definite quadratic function, in which the symmetric
positive semi-definite matrix is the learnable part in machine learning
applications. The second contribution lies on that we apply PDQK to Domain
Adaptive Learning (DAL). Our approach learns the PDQK through minimizing the
mean discrepancy between the data of source domain and target domain and then
transforms the data into an optimized RKHS generated by PDQK. We conduct a
series of experiments that the RKHS determined by PDQK replaces those in
several state-of-the-art DAL algorithms, and our approach achieves better
performance.


Abstract:
  There are still several unanswered fundamental questions concerning our
planet and in particular, about the deep Earth, from where we lack direct
samples. Today, due to the progress in neutrino-detection techniques, a new and
unique tool to study our planet exists: geoneutrinos, antineutrinos from the
decays of long-lived radioactive elements inside the Earth. In 2020, the
Borexino experiment published its updated geoneutrino measurement. Thanks to
the increase in acquired data and the improved analysis techniques in an
enlarged fiducial volume, the final precision has significantly improved,
corresponding to a total geoneutrino signal of 47.0$^{+8.4}_{-7.7}\,({\rm
stat)}^{+2.4}_{-1.9}\,({\rm sys})$ TNU. The null-hypothesis of the geoneutrino
signal from the Earth's mantle was excluded at a 99% C.L. for the first time,
while exploiting the knowledge of the local crust around the detector. The
article will introduce the field and describe the key elements of the updated
analysis. Geological interpretation and significance of the new result will be
discussed in terms of the corresponding radiogenic heat and the limits to a
hypothetical georeactor.


Abstract:
  This work presents an analysis of the Duckworth-Lewis-Stern (DLS) method for
One Day International (ODI) cricket matches. The accuracy of the DLS method is
compared against various supervised learning algorithms for result prediction.
The result of a cricket match is predicted during the second inning. The paper
also optimized DLS resource table which is used in the Duckworth-Lewis (D/L)
formula to increase its predictive power. Finally, an Unpredictability Index is
developed that ranks different cricket playing nations according to how
unpredictable they are while playing an ODI match.


Abstract:
  Plasmonic, as a rapidly growing research field, provides new pathways to
guide and modulate highly confined light in the microwave to the optical range
of frequencies. We demonstrate a plasmonic slot waveguide, at the nanometer
scale, based on high transition temperature superconductor BSCCO, to
facilitates the manifestation of the chip-scale millimeter waves to terahertz
integrated circuitry operating at cryogenic temperatures. We investigate the
effect of geometrical parameters on the modal characteristics of the BSCCO
plasmonic slot waveguide between 100 GHz and 500 GHz. In addition, we
investigate the thermal sensing of the modal characteristics of the nanoscale
superconducting slot waveguide and show that at a lower frequency, the
fundamental mode of the waveguide has a larger propagation length, a lower
effective refractive index, and a strongly localized modal energy. Moreover, we
find that our device offers a larger SPP propagation length and higher field
confinement than the gold plasmonic waveguides at broad temperature ranges
below BSCCO Tc. The proposed device can open up a new route towards realizing
cryogenic low-loss photonic integrated circuitry at the nanoscale.


Abstract:
  In this work, we develop a fast hierarchical solver for solving large, sparse
least squares problems. We build upon the algorithm, spaQR (sparsified QR),
that was developed by the authors to solve large sparse linear systems. Our
algorithm is built on top of a Nested Dissection based multifrontal QR
approach. We use low-rank approximations on the frontal matrices to sparsify
the vertex separators at every level in the elimination tree. Using a two-step
sparsification scheme, we reduce the number of columns and maintain the ratio
of rows to columns in each front without introducing any additional fill-in.
With this improvised scheme, we show that the runtime of the algorithm scales
as $\mathcal{O}(M \log N)$ and uses $\mathcal{O}(M)$ memory to store the
factorization. This is achieved at the expense of a small and controllable
approximation error. The end result is an approximate factorization of the
matrix stored as a sequence of sparse orthogonal and upper-triangular factors
and hence easy to apply/solve with a vector. Finally, we compare the
performance of the spaQR algorithm in solving sparse least squares problems
with direct multifrontal QR and CGLS iterative method with a standard diagonal
preconditioner.


Abstract:
  Two-dimensional electron and hole gases separated by a few nm from each other
are produced in p-i-n diodes based upon MBE-grown GaAs/AlGaAs heterostructures.
At such interlayer distances, the exciton formation and possibly Bose-Einstein
condensation (BEC) is expected. We measure the capacitance between the layers
and find it to oscillate as a function of the bias voltage. The peak values
exceed the geometric capacitance by up to a factor of two. The surprisingly
regular periods of the oscillations are of the order of 10 to 30 mV and scale
linearly with the inverse of the thickness, between 60 and 150 nm, of the GaAs
layer placed between the barrier and the p-doped AlGaAs. The oscillations are
related to the resonant electron tunneling into high energy levels of this GaAs
layer acting as a quantum well. We suggest that long lifetimes of the electrons
in these levels are the origin of the large peak values of the capacitance. The
possible relation of the capacitance oscillations with BEC is discussed.


Abstract:
  Significance: Histopathological analysis of tissues is an essential tool for
grading, staging, diagnosing and resecting cancers and other malignancies.
Current histopathological techniques require substantial sample processing
prior to staining with hematoxylin and eosin (H&E) dyes, to highlight nuclear
and cellular morphology. Sample preparation and staining is resource-intensive
and introduces potential for variability during sample preparation.
  Aim: We present a novel method for direct label-free histopathological
assessment of formalin fixed paraffin embedded tissue blocks and thin tissue
sections using a dual contrast photoacoustic remote sensing (PARS) microscopy
system.
  Approach: To emulate the nuclear and cellular contrast of H&E staining, we
leverage unique properties of the PARS system. Here the ultraviolet excitation
of the PAARS microscope takes advantage of DNA's unique optical absorption to
provide nuclear contrast analogous to hematoxylin staining of cell nucelli.
Concurrently, the optical scattering contrast of the PARS detection system is
leveraged to provide bulk tissue contrast analogous to eosin staining of cell
membranes.
  Results: We demonstrate the efficacy of this technique by imaging human
breast tissue and human skin tissues in formalin fixed paraffin embedded tissue
blocks and frozen sections respectively. Salient nuclear and extra-nuclear
features such as cancerous cells, glands and ducts, adipocytes, and stromal
structures such as collagen.
  Conclusions. The presented dual contrast PARS microscope enables label-free
visualizations of tissue with contrast and quality analogous to the current
gold standard for histopathological analysis. Thus, the proposed system is well
positioned to augment existing histopathological workflows, providing
histological imaging directly on unstained tissue blocks and sections.


Abstract:
  This paper presents a new evolutionary approach, EvoSplit, for the
distribution of multi-label data sets into disjoint subsets for supervised
machine learning. Currently, data set providers either divide a data set
randomly or using iterative stratification, a method that aims to maintain the
label (or label pair) distribution of the original data set into the different
subsets. Following the same aim, this paper first introduces a single-objective
evolutionary approach that tries to obtain a split that maximizes the
similarity between those distributions independently. Second, a new
multi-objective evolutionary algorithm is presented to maximize the similarity
considering simultaneously both distributions (labels and label pairs). Both
approaches are validated using well-known multi-label data sets as well as
large image data sets currently used in computer vision and machine learning
applications. EvoSplit improves the splitting of a data set in comparison to
the iterative stratification following different measures: Label Distribution,
Label Pair Distribution, Examples Distribution, folds and fold-label pairs with
zero positive examples.


Abstract:
  Alzheimer's disease (AD) is an irreversible, progressive neuro degenerative
disorder that slowly destroys memory and thinking skills and eventually, the
ability to carry out the simplest tasks. In this paper, a deep neural network
based prediction of AD from magnetic resonance images (MRI) is proposed. The
state of the art image classification networks like VGG, residual networks
(ResNet) etc. with transfer learning shows promising results. Performance of
pre-trained versions of these networks are improved by transfer learning.
ResNet based architecture with large number of layers is found to give the best
result in terms of predicting different stages of the disease. The experiments
are conducted on Kaggle dataset.


Abstract:
  Current radio frequency (RF) sensors at the Edge lack the computational
resources to support practical, in-situ training for intelligent spectrum
monitoring, and sensor data classification in general. We propose a solution
via Deep Delay Loop Reservoir Computing (DLR), a processing architecture that
supports general machine learning algorithms on compact mobile devices by
leveraging delay-loop reservoir computing in combination with innovative
electrooptical hardware. With both digital and photonic realizations of our
design of the loops, DLR delivers reductions in form factor, hardware
complexity and latency, compared to the State-of-the-Art (SoA). The main impact
of the reservoir is to project the input data into a higher dimensional space
of reservoir state vectors in order to linearly separate the input classes.
Once the classes are well separated, traditionally complex, power-hungry
classification models are no longer needed for the learning process. Yet, even
with simple classifiers based on Ridge regression (RR), the complexity grows at
least quadratically with the input size. Hence, the hardware reduction required
for training on compact devices is in contradiction with the large dimension of
state vectors. DLR employs a RR-based classifier to exceed the SoA accuracy,
while further reducing power consumption by leveraging the architecture of
parallel (split) loops. We present DLR architectures composed of multiple
smaller loops whose state vectors are linearly combined to create a lower
dimensional input into Ridge regression. We demonstrate the advantages of using
DLR for two distinct applications: RF Specific Emitter Identification (SEI) for
IoT authentication, and wireless protocol recognition for IoT situational
awareness.


Abstract:
  The paper has two relatively distinct but connected goals; the first is to
define the notion of Pad\'e\ approximation of Weyl-Stiltjes transforms on an
arbitrary compact Riemann surface of higher genus. The data consists of a
contour in the Riemann surface and a measure on it, together with the
additional datum of a local coordinate near a point and a divisor of degree
$g$. The denominators of the resulting Pad\'e--like approximation also satisfy
an orthogonality relation and are sections of appropriate line bundles. A
Riemann--Hilbert problem for a square matrix of rank two is shown to
characterize these orthogonal sections, in a similar fashion to the ordinary
orthogonal polynomial case.
  The second part extends this idea to explore its connection to integrable
systems. The same data can be used to define a pairing between two sequences of
line bundles. The locus in the deformation space where the pairing becomes
degenerate for fixed degree coincides with the zeros of a "tau" function. We
show how this tau function satisfies the Kadomtsev--Petviashvili hierarchy with
respect to either deformation parameters, and a certain modification of the
2--Toda hierarchy when considering the whole sequence of tau functions. We also
show how this construction is related to the Krichever construction of
algebro--geometric solutions.


Abstract:
  An unexpected explanation for neutrino mass, Dark Matter (DM) and Dark Energy
(DE) from genuine Quantum Chromodynamics (QCD) of the Standard Model (SM) is
proposed here, while the strong CP problem is resolved without any need to
account for fundamental axions. We suggest that the neutrino sector can be in a
double phase in the Universe: i) relativistic neutrinos, belonging to the SM;
ii) non-relativistic condensate of Majorana neutrinos. The condensate of
neutrinos can provide an attractive alternative candidate for the DM, being in
a cold coherent state. We will explain how neutrinos, combining into Cooper
pairs, can form collective low-energy degrees of freedom, hence providing a
strongly motivated candidate for the QCD (composite) axion.


Abstract:
  With the rapid adoption of Internet technologies, digital footprints have
become ubiquitous and versatile to revolutionise the financial industry in
digital transformation. This paper takes initiatives to investigate a new
paradigm of the unified credit assessment with the use of federated artificial
intelligence. We conceptualised digital human representation which consists of
social, contextual, financial and technological dimensions to assess the
commercial creditworthiness and social reputation of both banked and unbanked
individuals. A federated artificial intelligence platform is proposed with a
comprehensive set of system design for efficient and effective credit scoring.
The study considerably contributes to the cumulative development of financial
intelligence and social computing. It also provides a number of implications
for academic bodies, practitioners, and developers of financial technologies.


Abstract:
  Operational forecasting centers are investing in decadal (1-10 year) forecast
systems to support long-term decision making for a more climate-resilient
society. One method that has previously been employed is the Dynamic Mode
Decomposition (DMD) algorithm - also known as the Linear Inverse Model - which
fits linear dynamical models to data. While the DMD usually approximates
non-linear terms in the true dynamics as a linear system with random noise, we
investigate an extension to the DMD that explicitly represents the non-linear
terms as a neural network. Our weight initialization allows the network to
produce sensible results before training and then improve the prediction after
training as data becomes available. In this short paper, we evaluate the
proposed architecture for simulating global sea surface temperatures and
compare the results with the standard DMD and seasonal forecasts produced by
the state-of-the-art dynamical model, CFSv2.


Abstract:
  The augmented Iwasawa algebra of a p-adic Lie group is a generalisation of
the Iwasawa algebra of a compact p-adic Lie group. We prove that a
split-semisimple group over a p-adic field has a coherent augmented Iwasawa
algebra if and only if its root system is of rank one. We also characterise
when certain solvable p-adic Lie groups have a coherent augmented Iwasawa
algebra.


Abstract:
  An information cascade is a circumstance where agents make decisions in a
sequential fashion by following other agents. Bikhchandani et al., predict that
once a cascade starts it continues, even if it is wrong, until agents receive
an external input such as public information. In an information cascade, even
if an agent has its own personal choice, it is always overridden by observation
of previous agents' actions. This could mean agents end up in a situation where
they may act without valuing their own information. As information cascades can
have serious social consequences, it is important to have a good understanding
of what causes them. We present a detailed Bayesian model of the information
gained by agents when observing the choices of other agents and their own
private information. Compared to prior work, we remove the high impact of the
first observed agent's action by incorporating a prior probability distribution
over the information of unobserved agents and investigate an alternative model
of choice to that considered in prior work: weighted random choice. Our results
show that, in contrast to Bikhchandani's results, cascades will not necessarily
occur and adding prior agents' information will delay the effects of cascades.


Abstract:
  The modern Very Long Baseline Interferometry (VLBI) relativistic delay model,
as documented in the IERS Conventions refers to the time epoch when the signal
passes one of two stations of an interferometer baseline (selected arbitrarily
from the pair of stations and called the 'reference station', or 'station 1').
This model consists of the previous correlation procedure used before the year
2002. However, since 2002 a new correlation procedure that produces the VLBI
group delays referring to the time epoch of signal passage at the geocenter has
been used. A corresponding correction to the conventional VLBI model delay has
to be introduced. However, this correction has not been thoroughly presented in
peer reviewed journals, and different approaches are used at the correlators to
calculate the final group delays officially published in the IVS database. This
may cause an inconsistency up to 6 ps for ground-based VLBI experiments between
the group delay obtained by the correlator and the geometrical model delay from
the IERS Conventions used in data analysis software. Moreover, a miscalculation
of the signal arrival moment to the 'reference station' could result a larger
modelling error (up to 50 ps). The paper presents the justification of the
correction due to transition between two epochs elaborated from the Lorentz
transformation, and the approach to model the uncertainty of the calculation of
the signal arrival moment. The both changes are particularly essential for
upcoming broadband technology geodetic VLBI observations.


Abstract:
  Imputing missing values is an important preprocessing step in data analysis,
but the literature offers little guidance on how to choose between different
imputation models. This letter suggests adopting the imputation model that
generates a density of imputed values most similar to those of the observed
values for an incomplete variable after balancing all other covariates. We
recommend stable balancing weights as a practical approach to balance
covariates whose distribution is expected to differ if the values are not
missing completely at random. After balancing, discrepancy statistics can be
used to compare the density of imputed and observed values. We illustrate the
application of the suggested approach using simulated and real-world survey
data from the American National Election Study, comparing popular imputation
approaches including random forests, hot-deck, predictive mean matching, and
multivariate normal imputation. An R package implementing the suggested
approach accompanies this letter.


Abstract:
  Radio halos are synchrotron diffuse sources at the centre of a fraction of
galaxy clusters. The study of large samples of clusters with adequate radio and
X-ray data is necessary to investigate the origin of radio halos and their
connection with the cluster dynamics and formation history. The aim of this
paper is to compile a well-selected sample of galaxy clusters with deep radio
observations to perform an unbiased statistical study of the properties of
radio halos. We selected 75 clusters with M > = 6e14 Msun at z=0.08-0.33 from
the Planck Sunyaev-Zel'dovich catalogue. Clusters without suitable radio data
were observed with the Giant Metrewave Radio Telescope (GMRT) and/or the Jansky
Very Large Array (JVLA) to complete the information about the possible presence
of diffuse emission. We used archival Chandra X-ray data to derive information
on the clusters' dynamical states. This observational campaign led to the
detection of several cluster-scale diffuse radio sources and candidates that
deserve future follow-up observations. Here we summarise their properties and
add information resulting from our new observations. For the clusters where we
did not detect any hint of diffuse emission, we derived new upper limits to
their diffuse flux. We have built the largest mass-selected (> 80 per cent
complete in mass) sample of galaxy clusters with deep radio observations
available to date. The statistical analysis of the sample, which includes the
connection between radio halos and cluster mergers, the radio power-mass
correlation, and the occurrence of radio halos as a function of the cluster
mass, will be presented in paper II.


Abstract:
  Travel restrictions have often been used as a measure to combat the spread of
disease -- in particular, they have been extensively applied in 2020 against
coronavirus disease 2019 (COVID-19). How to best restrict travel, however, is
unclear. Most studies and policies simply constrain the distance r individuals
may travel from their home or neighbourhood. However, the epidemic risk is
related not only to distance travelled, but also to frequency of contacts,
which is proxied by the frequency f with which individuals revisit locations
over a given reference period. Inspired by recent literature that uncovers a
clear universality pattern on how r and f interact in routine human mobility,
this paper addresses the following question: does this universal relation
between r and f carry over to epidemic spreading, so that the risk associated
with human movement can be modeled by a single, unifying variable r * f? To
answer this question, we use two large-scale datasets of individual human
mobility to simulate disease spread. Results show that a universal relation
between r and f indeed exists in the context of epidemic spread: in both of the
datasets, the final size and the spatial distribution of the infected
population depends on the product r * f more directly than on the individual
values of r and f. The important implication here is that restricting r (where
you can go), but not f (how frequently), could be unproductive: high frequency
trips to nearby locations can be as dangerous for disease spread as low
frequency trips to distant locations. This counter-intuitive discovery could
explain the modest effectiveness of distance-based travel restrictions and
could inform future policies on COVID-19 and other epidemics.